Does Practicing with a Virtual Reality Driving Simulator Improve. Spatial Cognition in Older Adults? A Pilot Study

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(1)Does Practicing with a Virtual Reality Driving Simulator Improve Spatial Cognition in Older Adults? A Pilot Study by Sogol Masoumzadeh. A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba In partial fulfilment of the requirements of the degree of. MASTER OF SCIENCE. Graduate Program of Biomedical Engineering University of Manitoba Winnipeg. Copyright © 2020 by Sogol Masoumzadeh.

(2) Table of Content Abstract ......................................................................................................................................... XI Chapter One: Introduction .............................................................................................................. 1 1.1. Motivation ............................................................................................................................ 2 1.2. Thesis organization .............................................................................................................. 3 Chapter Two: Background .............................................................................................................. 4 2.1. Virtual reality technologies .................................................................................................. 4 2.2. Classification of VR environments ...................................................................................... 5 2.3. Game engines ....................................................................................................................... 6 2.4. Oculus Rift DK2 .................................................................................................................. 6 2.5. Simulation sickness syndrome ............................................................................................. 8 2.5.1. Simulation sickness theories ......................................................................................... 8 2.5.2. Simulation sickness in older adults ............................................................................. 10 2.6. Application of driving simulators in the aging population ................................................ 11 2.7. Summary ............................................................................................................................ 14 Chapter Three: Methodology ........................................................................................................ 15 3.1. The design components of the driving simulator ............................................................... 15 3.1.1. Graphical Processing Unit (GPU) ............................................................................... 15 3.1.2. Interaction paradigm ................................................................................................... 15 3.1.3. Displays....................................................................................................................... 16. II.

(3) 3.1.4. Simulation tool ............................................................................................................ 17 3.2. The design of the driving simulator ................................................................................... 18 3.3. The VRDS phases .............................................................................................................. 20 3.4. Pilot study protocol ............................................................................................................ 22 3.4.1. Assessments ................................................................................................................ 22 3.4.1.1 Virtual replica of the Morris water test ................................................................. 23 3.4.1.2 The VRDS’s spatial learning score system ........................................................... 25 3.4.2. Study participants........................................................................................................ 26 3.4.3 Intervention sessions .................................................................................................... 26 3.5. Data analysis ...................................................................................................................... 27 3.6. Summary ............................................................................................................................ 27 Chapter 4: Results ......................................................................................................................... 30 Chapter Five: Discussion .............................................................................................................. 40 Chapter Six: Conclusion, limitations and suggestions.................................................................. 44 6.1. Limitations and suggestions............................................................................................... 44 References ..................................................................................................................................... 47 Appendix A ................................................................................................................................... 55 Spatial Learning Score plots ..................................................................................................... 55 Healthy Participants .............................................................................................................. 55 MCI Participants ................................................................................................................... 56. III.

(4) Alzheimer’s Participants ....................................................................................................... 58. IV.

(5) List of Tables Table 1- Maximum error for each level of the VRDS .................................................................. 28 Table 2- Participants’ demographic information (mean ± stdev.) ................................................ 29 Table 3- Descriptive statistics (mean ± SE) of the evaluated variables, (MWT: Morris water test), *: Significant difference (p < 0.025) ............................................................................................. 33 Table 4- Participants’ descriptive information on using the VDS, MCI: Mild Cognitive Impairment, AD: Alzheimer’s disease .......................................................................................... 34 Table 5- Participants’ MADRS scores at baseline and post-intervention assessment sessions, MADRS: Montgomery-Asberg Depression Scale, MCI: Mild Cognitive Impairment, AD: Alzheimer’s disease ...................................................................................................................... 35. V.

(6) List of Figures Figure 1- Wingman Formula Force GP ........................................................................................ 16 Figure 2- The design of the VRDS, traffic lights and intersections.............................................. 19 Figure 3- The design of the VRDS, stop signs ............................................................................. 19 Figure 4- The design of the VRDS, sudden appearance of a deer ................................................ 20 Figure 5- The design of the VRDS, visual reward and fireworks ................................................ 21 Figure 6- VR Morris water test, performance metrices ................................................................ 36 Figure 7- Difference in the normalized correct trajectory from baseline to post-intervention, testing trials of the Morris water test (mean ± SE), Healthy participants (n=2), MCI participants (n=4) and Alzheimer’s participants (n=4) .................................................................................... 37 Figure 8- Spatial learning score plots of the participants’ subgroups. The red dot refers to the optimum learning: marked spatial learning score is 91% of the maximum spatial learning score for the 3rd level. ............................................................................................................................. 38 Figure 9- Spatial learning score plot, special case of the study. The red dot refers to the marked improvement: marked spatial learning score is 75% of the maximum spatial learning score for the 1st level. ................................................................................................................................... 39. VI.

(7) List of Equations Eq. 1 ............................................................................................................................................. 25 Eq. 2 ............................................................................................................................................. 25 Eq. 3 ............................................................................................................................................. 25. VII.

(8) Acknowledgements I would like to acknowledge my academic advisor, Dr. Zahra Moussavi for her mentorship during my studies. The day she accepted me as her student, marked a point in my life and changed my educational, professional and personal life path. Due to her trust, I have had the opportunity to expand my knowledge, learn more and gain various experiences in different aspects of life; even get to know myself, my interests, my dreams and goals better. I would be always sincerely thankful because of these opportunities and everything that I have learnt from her. I would like to thank Professor Brian Lithgow and Dr. Pourang Irani for accepting to be a member of my advisory committee. Their valuable advice and academic support assisted me to conduct this thesis and complete my final project in the best way possible. I would like to express my sincere gratitude and appreciation to Dr. Ian Jeffrey because of his academic support during my studies. Not only he taught me how to think critically and become a better programmer but also, he helped me to realize what I am really passionate about and what path I want to continue for my professional carrier. I would like to thank Dr. Kazushige Kimura for his friendship and support during my master’s studies. His valuable suggestions and advice helped me to successfully finish my coursework. More importantly, his suggestions for conducting this thesis, specifically in the design and development process of the necessary programs and software and also statistical analysis, are truly appreciated. In the end, I would like to thank all of my friends, research team members and all the others who have helped me in a way to successfully finish my master’s studies and make this period an unforgettable experience for me.. VIII.

(9) List of Abbreviations AD. Alzheimer’s disease. DK1. (Oculus Rift) Development Kit 1st Generation. DK2. (Oculus Rift) Development Kit 2nd Generation. GPU. Graphical Processing Unit. HMD. Head Mounted Display. IMU. Inertial Measurement Unit. LED. Light Emitting Diode. MADRS. Montgomery-Asberg Depression Scale. MCI. Mild Cognitive Impairment. MoCA. Montreal Cognitive Assessment. MWT. Morris Water Test. Sec.. Seconds. SSQ. Simulator Sickness Questionnaire. VR. Virtual Reality. VRDS. Virtual Reality Driving Simulator. VRN. Virtual Reality Navigation. VUs. Virtual Units. Yrs.. Years. IX.

(10) Dedication For my dear mother whom I always lean on in the hardest situations and share my happiest moments; who always has forgiven me no matter what mistake I have made.. For my father who is the backbone and strength of my life; who has always believed in me and supported me, no matter what.. For my younger brother who has brought me joy and happiness, since the first day I saw him.. And… For my dearest grandmother who our bond has been, is and always will be unreplaceable no matter what.. X.

(11) Abstract Many older adults experience memory, cognition and executive functioning impairments. A mild decline in cognition could be due to normal aging, while a moderate to advance decline could be due to dementia and in particular its Alzheimer’s disease subtype. One of the cognitive functions being impaired by Alzheimer’s is spatial cognition; an ability to navigate and orient in any new environment. Deficits in one’s spatial cognition have considerable negative impact on one’s quality of life and activities of daily living. Recently, the application of serious games as means for cognitive training to improve the brain’s lost functions has emerged significantly. The design of these serious programs is based on the neuroplasticity of the brain, with the purpose of targeting the brain’s impaired functions and improving them with repeated cognitive training. In this thesis, a driving simulator was designed in an immersive virtual reality environment (VRDS) with different levels of difficulty to be used as a serious game for improving older adults’ spatial cognition. We evaluated the effects of training with the VRDS on 11 volunteers with different cognitive status (range = 19, 21.1±5.9). Participants were trained with the VRDS for two weeks, every day except weekends (10 sessions of practice in total) and for 30 min/day. We used a virtual replica of the standard Morris water test as an independent assessment, to assess the participants’ spatial cognition before and after the two-week training intervention. Furthermore, through defining a spatial learning score, we evaluated the spatial performance of the participants and their progress in the game’s trials. Moreover, we evaluated the participants’ level of depression and their plausible simulation sickness using standard questionnaires at baseline and post-intervention. The results of the Morris water test demonstrated a significant improvement in participants’ spatial cognition; their normalized correct trajectory for finding the target of the test, improved significantly (+44.8%) at post-intervention in comparison to that at baseline. Furthermore, the. XI.

(12) participants had gradual progress in using higher (more challenging) levels of the game; their spatial learning score increased, with some participants reaching a marked improvement. The participants’ mood also improved after the two-week training program. Moreover, the repeated use of the VRDS, did not have any significant positive or negative effects on the participants’ experience of simulation sickness. Overall, the results hold promise for the designed VRDS as a serious game for mood-lifting and enhancing spatial skills of older adults if it is played regularly.. Keywords: Serious game, Cognitive training program, Virtual reality, Driving simulator, Spatial cognition, Dementia, Alzheimer’s disease.. XII.

(13) Chapter One: Introduction Memory, cognitive functions and executive functioning deterioration could be due to normal aging; however, a significant deterioration of these abilities could be due to dementia and specifically its Alzheimer’s subtype [1], [2]. It has been shown that seniors in comparison to younger adults demonstrate poorer performance in tasks associated with spatial cognition, while, cognitively impaired seniors perform worse in these tasks in comparison to their age-matched healthy peers [1], [3]. Based on the brain’s neuroplasticity, using cognitive training programs (that target the brain’s lost cognitive abilities) over a period of time and challenging the brain with complicated environments may lead to development of new functional connections within the brain; thus, helping patients to improve some of their impaired cognition [4], [5]. One of the recent neurocognitive rehabilitation methods is to use serious games designed specifically for improving cognition using a virtual reality (VR) environment [6], [8]. In two of our team’s previous studies, the application of VR serious games for cognitive rehabilitation of healthy individuals and those with mild cognitive impairment (MCI) and also individuals with Alzheimer’s was investigated with successful outcomes [6], [37]. The application of VR serious games and training programs has emerged in different fields of science such as medicine [9], [38], psychology and psychiatry [10], [11] and physical rehabilitation [12]. VR serious games have gained popularity as they are fun and engaging [13]. Furthermore, it is easy to reconfigure a virtual environment through manipulating its design parameters. Also, it is easy to create a safe training and/or experimental environment with different levels of difficulty for users with different cognitive status and different needs [10], [11].. 1.

(14) In this thesis, built upon the previous studies of our research team conducted on characteristics of navigation and spatial cognition [6], [14], [15], [17] a naturalistic virtual reality driving simulator (VRDS) was designed and used as a serious game for spatial cognition improvement in a training program for the older population with some memory problems and/or at different stages of dementia. Furthermore, the VRDS was designed as an engaging serious game for older adults, such that it could be used to improve their cognition while providing an uplifting enjoyable experience.. 1.1. Motivation Currently, the population of Canadians diagnosed with dementia is roughly 750, 000 [16]. This number is expected to be doubled by the year 2031 [16]. Canada has a high rate of the dementia prevalence attributable to Canada’s higher proportion of older adults [16]. There are various pathological reasons causing dementia including neurodegenerative diseases, tumors, brain’s vascular damage and also sensory and motor systems’ diseases such as Parkinson’s [18]. The most common type of neurodegenerative dementia is Alzheimer’s disease [18]; that can also be mixed with other types of dementia, such as vascular dementia [18]. The aggregation of extra cellular β-amyloid plaques and tau-protein neurofibrillary tangles inside of brain cells are causally linked to Alzheimer’s disease [18]. Alteration in the production of proteins associated with these plaques and tangles, protein over production, protein misfolding and failure in their subsidence leads to the deposition of the plaques and tangles within the brain [18]. As a result, the brain’s neurons can die, neurotransmitters would function differently and synaptic functions within the brain would alter [18]. To date there is no cure for Alzheimer’s or dementia in general. However, cognitive training programs (using serious games designed to target the brain’s impaired functions) offer. 2.

(15) some hope to strengthen the existing functional connections or regenerate new ones; thus, improving impaired cognitive function [4], [5]. Furthermore, a considerable population of individuals affected by dementia suffer from depression [20]. Many of them are prohibited to drive because of poor cognition, and that alone often makes them feel depressed. Thus, the goal of this thesis was to design a driving simulator using VR, both as a neurocognitive rehabilitation tool to improve its users’ spatial cognition and also as an uplifting game.. 1.2. Thesis organization This thesis is made up of six chapters. Following the current chapter, is the background, which focuses on the fundamental concepts and principles such as VR, simulation and immersion tools. Initially, VR technologies and environments are introduced; then classified based upon their immersion level. Secondly, game engines are introduced and their application as the main simulation tool is discussed. Afterwards, the Oculus Rift head mounded display (HMD) as the main tool providing immersive virtual exposure is described following by the description of simulation sickness syndrome as the main adverse effect of using Oculus. Lastly, the application of VR driving simulators is discussed and previous relative studies are reviewed. The third chapter of the thesis is dedicated to the methodology used for designing and developing the VRDS. Furthermore, a cognitive training pilot study is described in detail in chapter three. The results of that pilot study are presented in chapter four and chapter five discusses these results. The last chapter of the thesis concludes the outcomes of the thesis and presents limitations, future considerations and recommendations.. 3.

(16) Chapter Two: Background Due to recent advancements in different fields of computer engineering and computational power, VR technology has progressed significantly and gained popularity [13], [21]. The VR technology not only has emerged as an entertainment tool, but also has been applied in different fields of science specifically in neuroscience and cognitive rehabilitation [6], [13], [37]. This chapter of the thesis focuses on the definition of VR and its associated concepts and presents the application of VR driving simulators.. 2.1. Virtual reality technologies Development and progress of practical photography and the creation of panoramic photos in 19th century is assumed to be the VR’s starting point [22], [23]. However, the crucial milestone for VR emergence and popularity is dated back to 1957 and the invention of Sensorama [11], [22]. Through multi-sensory simulation, Sensorama engaged its users to have an experience close to reality [11]. In 1965, Ivan Sutherland introduced Ultimate Display, which according to Sutherland “served as a window to the virtual world” [13], [23]. Ultimate display showed a rendered image in a fashion that was presumed to be natural [13]. Also, users were able to interact with Ultimate display [11]. The invention of the first technical HMD in 1961 was another step towards what we know as VR today [11]. Through interaction with a VR technology, users are immersed within a computer environment that is designed to be naturalistic [21]. For designing naturalistic immersive environments, developers use 3D realistic images, image processing techniques, and fractal landscapes [13], [24]. Presence, defined as the sensation of being invested within an environment, is used to quantify the immersion level of a virtual environment [21]. It is possible to manipulate one’s presence through conducting special experiments [25]. In these types of experiments,. 4.

(17) participants’ self-localization and self-attribution associated with their whole body or a specific body part is manipulated such that for example they would attribute a second object, for instance a rubber hand, as their own [25]. Alongside of technical and design properties of a VR application, the VR technology’s interaction paradigm has a considerable influence on its immersion level [14], [21], [26]. With the help of the interaction paradigm, which is a combination of displays (regular monitors or immersive displays) and input devices, users communicate with the VR system [21]. The interaction paradigm can be of a standard type using a computer screen and usual input devices such as mouse, keyboard, joystick or gamepad [13], or it can use a novel design, such as VRN chair [14], as an input device. In a study conducted by one of our previous team members, a manual wheelchair was modified to be used as the input device for a VR system [14]. The designed VRN Chair allows users to move in a natural environment physically, while their movement is simulated simultaneously in a VR environment [14].. 2.2. Classification of VR environments VR systems can be classified as non-immersive, semi-immersive and fully immersive [21][27]. Non-immersive and semi-immersive VR environments have a standard interaction paradigm. The size of computer displays, and the quality of graphics are the main differences between these two systems. In comparison to non-immersive VR systems, semi-immersive ones have larger displays and use more realistic graphics. On the other hand, the main property of fully immersive VR systems is their head-tracking ability, which is provided by their immersive displays [21]. Other than usual input devices, immersive displays can be paired with various tracking technologies for different body parts, such as HTC Vive, to provide a complete immersive experience [19], [21].. 5.

(18) 2.3. Game engines The term game engine appeared in the late 90s when the popular first-person shooter game Doom dominated the gaming industry [28]. The significant characteristic of Doom was the separation of its gameplay from its engine software, i.e. collision detection, graphics and audio rendering systems [28]. Because of this separation, designers and developers were able to focus on artistic design and story invention, while only applying few necessary modifications to the already available software to correspond with the properties of their game. For designing a naturalistic, intractable and immersive VR environment, it is possible to use an advanced game engine for exploiting its rigid bodies and collision detection, vison and sound synthesis systems [13], [29]. Furthermore, by using an existing game engine, the development time of the VR system decreases considerably [29]. Knowledge of the game engine’s characteristics and its lower level implementations is a necessary factor for choosing a suitable game engine to match with the developer’s goals [28].. 2.4. Oculus Rift DK2 Oculus Rift development kit second generation, in short DK2-2014, is a vivid example of an advanced, low cost and consumer level HMD that can be used as the display in fully immersive VR systems. In comparison to the previous generation (DK1), DK2 hardware and structure have improved significantly. Oculus Rift Dk2 has a higher resolution and consists of 1920 X 1080 pixels per eye[30]. DK2 uses larger lenses providing an expanded field of view; hence, users are able to experience more immersion [21]. Other DK2’s characteristics affecting presence and immersion are lighting specifically shadows, texture rendering technologies and being a fast device with minimal latency [13]. Other than mentioned properties above, the main effective characteristic of. 6.

(19) DK2 on immersion is its head tracking ability. The head tracking system allows users to look around the virtual environment; hence, their feeling of presence and immersion are improved [21]. Generally, an HMD locates the head based upon two approaches: outside-in and inside-out. In the first approach an external tracking device i.e. an infra-red camera, records the head’s movements [13], [21]. Then, the data is sent to the display for analysis and calculation of the head’s position within the space. Due to hardware configurations and the placement of the camera, it is recommended to use this approach for stationary tasks [21]. On the other hand, in the second approach, the inside-out algorithm uses an independent position tracking device for each of the goal objects. As each tracked object is mounted with its own tracking technology, this algorithm has the benefit of scalability [21]. In comparison to the former approach, the inside-out algorithm is more complex and accurate [21]. Oculus Rift DK2 has a head tracking system which is a combination of outside-in and Inertial Measurement Units (IMUs) [21], [30]. Several lighting emitting diodes (LEDs), each one blinking in a specific pattern, are embedded within the DK2 screen [30]. The external camera records the LEDs’ lighting patterns and sends the data to the HMD for head localization. Through comparing the received data and the screen position of the LEDs, Oculus localizes the head position. However, the position extraction, although it is done in real time, is not as fast as the movement of the head [21], [30]. For aligning the head’s movements’ speed and the DK2’s frame rate, IMUs have been embedded within the headset [21]. IMUs consist of accelerometers, gyroscopes and magnetometers [17], [21]. Using IMUs, it is possible to calculate the head’s rotational movement (3 degrees of freedom) accurately and to extract the position of the head between data updates of the external camera; i.e. calculate the head’s transitional movement [21]. Hence, the transition of scenes in the HMD is done smoothly. 7.

(20) and without any lag time. Moreover, DK2 has the ability to only use its IMUs for the head tracking [21]. In other words, Oculus can operate in the inside-out mode without using the external camera.. 2.5. Simulation sickness syndrome While using virtual environments has multiple benefits such as easy, effective and risk-free environment manipulation [10], it can also have the well-known simulation sickness side effect. It is most likely for a user, specifically an older adult, to experience simulation sickness during or after exposure to an immersive VR system [21], [31], and [32]. Simulation sickness is a syndrome that is characterized through symptoms such as disorientation, nausea, headache, dry mouth, sweating and disturbance of the eyes’ movements [31], [33]. The severity of the symptoms can be to a level that affects the study outcomes and/or increases the number of withdrawals. Thus, simulation sickness is an important confounding variable that is needed to be addressed in VR studies [31]. Demographic characteristics of VR systems’ users such as their age, gender and previous experience in using a VR system can also affect their experience of simulation sickness. There are three main theories attempting to reason the simulation sickness syndrome; they are described below. 2.5.1. Simulation sickness theories 1. Sensory mismatch theory This theory describes the most popular reason for the simulation sickness syndrome. According to this theory, simulation sickness occurs when there is a conflict between sensory systems [26], [31], and [32]. If one’s observations (visual stimuli) do not line up with the movement they are experiencing (vestibular and proprioceptive stimuli), brain reacts as if it is poisoned and tries to eject the poison using fast and powerful movements of stomach and gag reflexes [21], [31]. From another point of view, if one receives sensory information which 8.

(21) significantly differs from his/her neural memory, i.e. how s/he expects the movement to be within that environment, one experiences simulation sickness [31]. 2. Postural instability theory According to this theory, one is familiar with the possible range of movements in his/her usual environment; hence, s/he can keep his/her balance with minimal effort [31]. On the other hand, for a person who is present in a new and unusual environment, for instance a boat in a sea with movements that have not been seen and learnt before, it is hard to keep his/her postural balance. As a result, s/he may experience simulation sickness [31]. One can argue against this theory that some people even in sleeping position may experience simulation sickness due to vertigo [26]. 3. Eye movement theory According to this theory, certain visual stimuli can cause specific eye movements resulting in the stimulation of the Vagus nerve and consequently simulation sickness [31]. For instance, while turning the head, if one fixes his/her attention on an object, his/her eyes move with accordance to their head’s movement, i.e. with the same degree of rotation and to the same direction. While being exposed to an immersive VR environment, due to the latency of the immersive display, there is a chance that this eye movement is not aligned with the head rotation. This unalignment triggers the Vagus nerve to activate some physiological behaviour, which is presumed necessary for survival in the present condition but simultaneously it causes simulation sickness [31]. As the argument against this theory, even when a person closes his/her eyes, s/he is still susceptible to experiencing simulation sickness [26].. 9.

(22) 2.5.2. Simulation sickness in older adults There is contradictory information upon the prevalence of simulation sickness among older adults [31], [34], [35], and [36]. According to a number of studies, older adults experience more severe simulation sickness incidents than younger adults [31], [34], and [36]. This difference may be reasoned based on the postural instability theory [31]. In another research, older adults with spatial cognition impairments were affected by simulation sickness more than their healthy peers or seniors with milder degree of spatial impairments [36]. On the other hand, our experience with Alzheimer’s participants has shown that in comparison to volunteers at earlier stages of the disease, participants with advanced stages of Alzheimer’s, experience less simulation sickness incidents. This might be due to anatomical changes in their vestibular and balance system [35]. For reducing the risk of simulation sickness, it is possible to expose the VR users to the system gradually, so that to give the body a sufficient amount of time to get used to the unfamiliar environment [21], [36]. Moreover, it has been demonstrated that continuous exposure to VR up to an hour would increase the severity of simulation sickness. But, after an hour period of adaptation, and only within 15 minutes of additional exposure , i.e. 1.15 hour of exposure in total, the simulation sickness severity would decrease considerably to a level similar to the baseline severity [31]. It is important to note that seniors are unlikely to be capable to tolerate such a long adaptation period. Nevertheless, conducting repeated practice sessions with a VR system, even short in duration, is claimed to lead to VR adaptation and reducing the severity and occurrence of simulation sickness incidents over time [36]. The users’ age is an important consideration in VR studies as older adults usually need more time to be adapted to a VR system [36], or they may not be able to tolerate an immersive VR environment even for a short period of time.. 10.

(23) For reducing the probability of simulation sickness occurrence, it is possible to explore novel design-orientated considerations. For instance, it is possible to use innovative input devices in the interaction paradigm for omitting the sensory conflicts between visual and vestibular systems [14]. As an example, the earlier mentioned VRN Chair was shown to be significantly effective for reducing the simulation sickness severity compared to other common and also two other novel input devices including a tilt chair and an omnidirectional treadmill [14], [26]. Moreover, using an advanced HMD with high frame rate and smooth transition between scenes, along with increasing the immersion, would possibly decrease the risk of simulation sickness [31][32].. 2.6. Application of driving simulators in the aging population One of the earliest signs of dementia, in particular Alzheimer’s is one’s deteriorated ability to orient and navigate in an unfamiliar environment. It has been shown that this skill does deteriorate by normal aging but much more significantly by Alzheimer’s [3], [15], [40], and [41]. In general, healthy older adults have a poor performance in landmark recognition and path recalling in comparison to younger people [40]. In addition, individuals with Alzheimer’s have a significant difficulty in recalling the objects within an environment and reorienting themselves once they are perturbed by a change in the environment or a simple turn [40]. At more advanced stages of the disease, individuals may even get lost in their own neighborhood. The early studies with the focus on orientation and spatial navigation are dated back to 80s. The famous study conducted by Morris [42], known as Morris water test aimed to investigate spatial cognition in rodents. Using a pool of water and different architecture of platforms as the target, rats were placed at different locations inside the pool to navigate the area and find the target using spatial learning. Their behavior of using the distal objects present in the experiment room as. 11.

(24) cues for learning the target’s position and escaping the water, were studied [42]. Nowadays, this test has become a standard assessment for investigating spatial cognition in human participants and animal subjects [55], [56], and [57]. It has been demonstrated that the evaluation and the practice of navigational abilities in VR is as valid as the real-world settings [40]. Although users’ spatial performance in a VR environment is usually poorer than that in a real-world spatial task, their VR navigation performance can be used to predict and train their real-world navigation [40]. Built upon the evidence demonstrating the effectiveness of VR navigational rehabilitation programs [40] and our team’s expertise in designing and evaluating VR navigational and spatial tasks [6], [14], [15], and [17] the focus of this thesis was to design and develop a VR driving simulator with different levels of difficulty to be used as a spatial cognition rehabilitation tool for older adults with some memory problems and those with dementia. A virtual driving simulator is a class of VR serious games, which has various applications including the evaluation of one's hazardous driving habits [2], [43]. For instance, using a semiimmersive driving simulator, researchers have demonstrated that Alzheimer’s participants have an increased reaction time in mental flexibility tasks in comparison to age -matched healthy participants; as a compensation to this reduced ability in mental flexibility, they drove with a slower average speed [2]. In another similar study [44], visual attention and VR driving performance were found to affect one another. Researchers found that training with a commercial semi-immersive driving simulator would positively affect older adults’ visual attention. Furthermore, they investigated the effects of simulator training on the participants’ physical mobility. The results showed no relationship between the physical mobility and training with the driving simulator [44].. 12.

(25) Another study used a non-immersive driving simulator to compare driving performances of three groups of participants: healthy older adults as the control group, individuals with MCI and those with possible Alzheimer’s [45]. The results showed that both groups of MCI and Alzheimer’s had a poorer driving performance in comparison to the control group. The errors of these groups were mainly due to attention deficit, false judgment and/or being too cautious. Using driving simulators, the driving behavior of MCI population has been evaluated [46][47]. For instance, other than investigating the relationship between executive functioning and the driving performance of MCI population [2], various levels of in vehicle-distractions and their effects on the driving of MCI individuals in VR have been evaluated [46]. It was demonstrated that MCI individuals in the study, were more prone to distractions such as talking on a cellphone compared to an age-matched control group. Another similar study evaluated MCI drivers’ estimation of their driving performance; compared it to their actual simulator driving metrics [47]. That study demonstrated that MCI participants did not judge their driving abilities fairly and likely overestimate it. Also, driving simulators are used to investigate the biomechanical aspect of driving among healthy adults and seniors [48]. An immersive driving simulator was used to evaluate the neck and the whole upper body rotation and their effects on participants’ performance in the task of blind spot checking [48]. It was shown that the upper body rotation was greater in comparison to the neck’s range of motion. Furthermore, seniors had a poorer performance in the driving task in comparison to younger adults [48]. Furthermore, in one of our team’s previous studies, a simple immersive driving simulator was used for an individual at the onset of dementia to encourage him for continuing his other cognitive training programs that he had been enrolled in [21]. The results. 13.

(26) of that program showed the participant improved in simulated driving task, and also enjoyed the practice sessions immensely.. 2.7. Summary In this chapter, the necessary information and background of VR environments’ designs and applications were overviewed. Virtual environments were defined, and different methods for interacting with them were discussed. The architecture of head tracking systems in HMDs and particularly Oculus Rift DK2 was introduced. Furthermore, the simulation sickness syndrome as a side effect of VR systems was overviewed. In addition, the literature associated with different applications of VR driving simulators such as the evaluation of senior’s driving abilities and the relationship between these abilities and users’ cognition and physical abilities were overviewed.. 14.

(27) Chapter Three: Methodology In this chapter of the thesis, the design of the VRDS, as a cognitive training serious game for older adults with different levels of dementia, is described. Furthermore, the protocol of a pilot study to evaluate the effects of the VRDS on older adults’ spatial cognition and mood is elaborated. The analytical approach of the study, including the analysis of the game metrics and the results of independent assessments, is elaborated as well.. 3.1. The design components of the driving simulator To design the driving simulator, the VRDS, following components are used. 3.1.1. Graphical Processing Unit (GPU) GPU’s properties such as its rendering technologies, power and pixel rate affect the designed virtual environment’s immersion level [49]. In general, the more advanced the GPU’s rendering technologies, the higher immersion is anticipated from its associated VR system. Our driving simulator is rendered on a personal laptop, using a NVIDIA GTX980M G-SYNC GPU, which is a standard GPU for gaming purposes with a high rendering power. Following subsections describe the interaction paradigm of the VRDS including its displays and input devices. Furthermore, the game engine used for simulating the VRDS is introduced. 3.1.2. Interaction paradigm The interaction paradigm of the VRDS consists of two displays, one immersive and one non-immersive using a regular laptop screen, and Wingman Formula Force GP as the primary input device. It is also possible to control the VRDS using keyboard’s arrow keys. The Wingman Formula Force GP is consisted of a steering wheel and two pedals. The right pedal is acceleration while the left pedal is assigned for backing up. For braking, a trigger button. 15.

(28) at the back of the steering wheel is used. The Wingman Formula Force GP does not have a gear box; hence, the simplest practice for backing up is to assign the reverse direction to the left pedal and assign the brake to one of the buttons of the steering wheel (Figure 1). Figure 1- Wingman Formula Force GP. 3.1.3. Displays The VRDS is designed to operate as a fully immersive VR system; thus, it should support an immersive display such as an HMD [21], [27]. For this study, Oculus Rift second generation, DK2-2014, was chosen as the immersive headset. Its immersive characteristics including its powerful rendering technologies, low latency and high refresh rate of the display, naturalistic lighting and shadows, and also its active user community have been considered for choosing this HMD as the main immersive display [13], [49]. While using the VRDS with Oculus, it is possible to switch the mode, for using the game with laptop screen. The reason for this dual mode of operation is to prevent users to experience simulation sickness. Research has demonstrated that users of fully immersive VR environments are more susceptible to experience simulation sickness in comparison to users of non/semiimmersive VR systems [32].. 16.

(29) 3.1.4. Simulation tool For developing the naturalistic environment of the VRDS, we used Unity 3D game engine and integrated development environment, version 5.1.3 and its photo realistic 3D images and assets [50]. Unity 3D is mainly used to develop interactive environments and specifically video games [51]. The first version of Unity was launched in 2005. The goal of the producers was to develop a toolset to make the game development easy, targeting novice game developers and developers who are not able to afford luxury game development tools [51]. Unity provides developers with advanced graphical features including noise reduction filters and naturalistic lighting effects [21]. Moreover, due to Unity’s collision physics, visual and sound synthesis systems, it is possible to interact with users effectively and increase the immersion level of the VR system considerably [13], [29]. Furthermore, source codes of a Unity program are consisted of managed codes [21]. A managed code is written using a high-level language, C# in the case of using Unity 3D. Through compiling these codes, intermediate language files are developed [52]. Simultaneous with runtime process of these intermediate files, they are converted to binary machine language [52]. This process of compiling the managed codes to intermediate language files and then converting them to binary programs, has considerable communication overhead; however, this process let the run time language to automatically mange the memory. Even security considerations are applied automatically through the runtime process. On the other hand, using languages such as C/C++ for writing computer programs, memory management and security considerations are the responsibility of programmers [52]. For designing a game environment, Unity provides the designers with various free 3D models and assets. In this study, a standard asset was used to simulate the virtual vehicle. We modified the source codes of the vehicle asset, including its control and drive mode to make them suitable for cognitive abilities of the intended users. Free 3D models, Industrial Sign pack and 17.

(30) Kajamans Roads [ref] were used as streets and traffic signs. Moreover, for simulating a rural animal in our VRDS environment, an animated model of a deer, Forest Animal-Deer Doe [ref], was used.. 3.2. The design of the driving simulator The VRDS is a serious game with three levels of difficulty and a naturalistic rural route. The first level’s route is 600 virtual units (VUs) long. Virtual unit is an arbitrary unit for distance and is assigned to be equivalent to one unit of distance in real world. In current design, one unit of VU is equivalent to one meter in natural environment. The second level and the third level are 920 VUs and 1320 VUs, respectively. Based on the level of the game, the route has a number of intersections. For the first level, one intersection and for the second and third levels, two and three intersections are designed. Serval traffic elements are placed within the road. At each intersection, there is a traffic light (Figure 2). The traffic lights are programmed based on the state-machine concept. The state of the light is initially set as green. When the virtual vehicle gets close enough to the light, 30 VUs from the prospective intersection, the state of the traffic light changes to red. Afterwards, with 0.1 Hz frequency, the state of the traffic light alternates periodically between red and green. In each level, at few points of the road, there are stop signs (one sign for the first two levels and two stop signs for the last level). It is anticipated from users to stop before the stop line when they encounter a sign (Figure 3). After checking the road for upcoming traffic, if the road was safe, they may proceed with their driving. Furthermore, at few points of the road, an upcoming vehicle is placed in the opposite lane.. 18.

(31) Figure 2- The design of the VRDS, traffic lights and intersections. For each level, a number of checkpoints are placed at specific points of the road. The number of checkpoints for the first level is 21 and for the second and last levels are 30 and 40, respectively. While driving, the virtual vehicle passes some of these checkpoints. The indices of the passed checkpoints, the time when the virtual vehicle passed the checkpoints, its speed and location within the virtual environment in Cartesian system and with respect to Unity’s axes are logged (in a CSV file). In addition, the number of crashes between each two consecutive passed checkpoints, if any, are reported in the CSV file. This logged CSV file is specific for each user and each trial the user plays the game; it is created using an interactive input field before the start of the training trial. Using the logged CSV file, it is possible to evaluate whether users have stopped properly for the traffic lights and stop signs. Figure 3- The design of the VRDS, stop signs. 19.

(32) Assigned to each traffic light is a pair of variables, consisted of a Boolean and the state of the light, that is written in the CSV file when the vehicle passes the intersections. For instance, if the pair reads true/green, it means the user stopped for the red signal and started driving after the signal changed to green. Meanwhile, if the pair reads true/red, although the user stopped before the red signal, s/he did not wait long enough and passed the red signal anyways. Similarly, using a logged Boolean variable, it is possible to decide whether or not the user stopped properly for a stop sign. At the beginning of each level, sudden appearance of a rural animal is simulated. In about 90 VUs from the start line, a deer is hidden in bushes. When the vehicle gets close enough to the deer (12 VUs), the deer jumps out of the bushes and starts running to the other side of the road. If the user reacts right upon observing the deer and brakes instantly, assuming the car is moving with the maximum speed of 20 Km/hour, the car will stop after 1 sec. and 5.55 VUs from the deer (Figure 4). Figure 4- The design of the VRDS, sudden appearance of a deer. 3.3. The VRDS phases The VRDS has two phases, demo and training, which can be chosen from an interactive menu at the beginning of the game. During the demo, a vehicle drives automatically on the road. 20.

(33) and upon reaching each intersection, it turns to a predefined direction. Using an audio message, users are instructed to memorize the direction that the vehicle travels, specifically for the turns in intersections. Five seconds after the demo finishes, the game loads the main menu automatically. During the training phase, by an audio message, users are asked to recall the traversed path in the demo and drive the same path. If users turn to a wrong direction at an intersection, they are warned using an audio message to go back to the correct route. Users are able to track their duration of driving using a timer placed at the up-right corner of the display. Furthermore, if users finish a level, a reward audio message accompanying a visual reward with fireworks are displayed for users, and after 5 sec. the game loads the main menu again (Figure 5). For improving the presence and the immersion of the VRDS, traffic sounds are added to the training phase of the VRDS. Figure 5- The design of the VRDS, visual reward and fireworks. At the beginning of the training and before the game loads the first level, a graphical instruction on using the VRDS is displayed for users. While playing with the game, users can pause the game to activate a second menu by pressing the Escape key on the keyboard. From this interactive menu, users can choose to go back to the main menu to choose a different level or a. 21.

(34) different phase of the game. They can also quit the game if they wish to. By pressing the Escape key for a second time, the menu is deactivated, and the game continues from where it was paused.. 3.4. Pilot study protocol For investigating the effects of the repeated practice with the VRDS on its users’ spatial cognition and mood, a pilot study was conducted. We evaluated our VRDS training program on 11 participants (3 males, 77±10.1 years) with different levels of dementia. 3.4.1. Assessments In this pilot study, three questionnaires including Montreal Cognitive Assessment (MoCA) [53], Montgomery-Asberg Depression Scale (MADRS) [54] and Simulator Sickness Questionnaire (SSQ) [33] were used. Moreover, a virtual replica of the standard Morris water test was used as the independent assessment for evaluating the participants’ spatial cognition [21]. One of the widely used neuropsychological assessments for quantifying the cognition level of a person is MoCA [53]. MoCA is a 30-point test to assess cognitive functions. This test takes approximately 10 minutes to administer; screens for MCI and possible dementia. A score of 26 or higher is considered as healthy. This test assesses participants based upon their language, memory, attention, orientation and visuospatial abilities [53]. MoCA was used only at baseline and for screening the participants’ cognition status. MADRS questionnaire [54] is a ten-item scale for rating depression levels. Each item is rated on a 7-point Likert scale from 0 to 6, where 0 indicates the absence of the symptom and 6 indicates the extreme presence of depression. The time frame for the questionnaire is the previous four weeks. This test takes approximately 15-20 minutes to administer. The total score of 10 or higher indicates the possible presence of depression. This test was used at baseline and postintervention to investigate whether playing with the VRDS has improved its users’ mood.. 22.

(35) For investigating the effects of the VRDS on the severity of the participants’ simulation sickness experiences, SSQ was run after the first and last session of training with the driving simulator. SSQ is a sixteen-item questionnaire for rating the severity of most common symptoms associated with simulation sickness categorized to nausea, disorientation and oculomotor [33]. Each item is rated on a 4-point Likert scale from 0 to 3, where 0 indicates the absence of the symptom and 3 indicates the symptom being severe. 3.4.1.1 Virtual replica of the Morris water test For evaluating the effects of a cognitive training program, such as this pilot study, on a particular cognitive function (i.e. spatial cognition), an assessment specific to that cognition fulfilling its natural properties, should be used. There are a number of paper and pencil psychological assessments for evaluating spatial cognition. But these tests do not transfer the feeling of movement, which is the main property of the natural spatial task used in this pilot study. Furthermore, in these assessments, participants observe the whole test’s environment, mostly a piece of paper, from above. But in natural spatial tasks, participants observe a portion of the environment from a first-person point of view [41]. Due to mentioned discrepancies between the natural and real-world spatial tasks and the paper-based assessments specific for spatial cognition, we decided to use a spatial assessment with more similarities to the natural settings; we used the Morris test in an interactive VR. This assessment is a non-immersive virtual replica of the standard Morris water test [42] and is one of our team’s previous Unity 3D designs [21]. The effectiveness of this test in assessing humans’ spatial cognition has been demonstrated in [55], [56], and [57]. In the current replica of the test [21], participants are instructed to locate a fixed-position hidden target within a circular arena of radius 5 VUs, by moving around the environment (using keyboard’s arrow keys) and using the distal cues present in the environment, such as different trees, that are learnt and practiced during the training trials. The location of the target in all of the trials 23.

(36) is fixed and is at the middle of the Northwest or Southwest quadrant of the arena. The target location is counter-balanced between participants and baseline and post-intervention assessment sessions to minimize the assessment’s learning effects. On the other hand, in each trial, participants’ starting location alternates between South, North, East and West. This test has four training and one test trials. In training trials, if users are not able to locate the target before 45 sec., the target becomes visible to participants. Thus, they could learn and memorize its location with respect to distal cues. In the test trial, participants have to locate the target within 45 sec., before the trial ends automatically. The target is not shown to participants in the end. To evaluate the performance of the participants in the VR Morris water test, the traversed trajectory of each participant in the test trial was plotted, and the total traversed path and the correct trajectory were calculated. The correct trajectory is defined as the part of the total trajectory that occurs in the quadrant of the target. We normalized the correct trajectory with respect to the total trajectory and defined it as the performance metric for the testing trials of the VR Morris water test. Moreover, for each training trial of this test, the total time spent for finding the target was also calculated as another evaluation metric. We performed this test at baseline and post-intervention to evaluate the far effects of the repeated practice with the VRDS on the participants’ spatial cognition. Evaluation of a cognitive training serious game and/or program, such as our VRDS, can be done from two different but related perspectives: far and near effects of the program’s repeated usage. Far effects are the plausible improvements measured by an independent assessment (but conceptually similar to the training task) to what the training is focused on; that is the VR replica of the Morris water test in our study. Near effects are the improvements (if any) achieved on the. 24.

(37) trained tasks during a cognitive training program; that is the spatial learning score system defined for the VRDS. This scoring system is described in the following section. 3.4.1.2 The VRDS’s spatial learning score system We evaluated the participants’ progress while training with the VRDS as the near effects of our VRDS cognitive training program. Using the logged information in the data CSV files of the game, it is possible to judge whether the participants have braked for the red signals and stop signs properly and/or whether they have turned to the correct directions at intersections. Using this information, we defined a (spatial) learning score as follows:. Error = 2 X direction error + traffic light error + stop sign error. Eq. 1. Error score = Error / Maximum error of the level. Eq. 2. (Spatial) learning score = level X (1– error score). Eq. 3. In Eq. 1, the direction error corresponds to the situation in which participants turn to a direction other than what has been demonstrated in the demo phase of the driving simulator. Moreover, traffic light and stop sign errors demonstrate a situation in which participants did not stop properly for the red signals and stop signs. In Eq. 2, the maximum error of a level corresponds to when users turned to wrong directions at all intersections and did not stop for any of the red signals and stop signs. Table 1 demonstrates the maximum error for each of the game’s levels. The overall spatial learning score, defined in Eq. 3, considers the difference in difficulty of the levels, and assigns more weight to the learning at more challenging levels.. 25.

(38) 3.4.2. Study participants Total number of 11 volunteers, with some memory problems have been recruited from the residents of the Lindenwood Retirement community Winnipeg, Manitoba (n=5) and the volunteers of our team’s previous studies. The first part of the study was taken place in September 2019; with the help of the volunteers from the Lindenwood Retirement Community. The second half of the study started from mid-October 2019; was conducted with the participants who have been previously enrolled in one of our team’s cognitive training programs. At baseline session, we assessed the participants’ cognition using MoCA. Out of the 11 participants, three of them were healthy with MoCA > 25, four had 21 < MoCA < 25 (inclusive) and were considered as MCI, and four participants had MoCA < 21 and were diagnosed with early to moderate stage of Alzheimer’s. In addition, we enrolled one participant (77 yrs) with advanced Alzheimer’s and a MoCA score of 2 as a special case. All participants (or their legal guardians in the case of Alzheimer’s volunteers) signed an informed consent form approved by the Biomedical Research Ethics Board of the University of Manitoba prior to being enrolled into the study. Table 2 demonstrates the demographic information of the participants. 3.4.3 Intervention sessions All study participants practiced with the VRDS for 2 consecutive weeks, everyday (excluding weekends) for 30 mins/day (10 practice sessions in total). During the intervention sessions, a trained research assistant was present all the time to monitor for the plausible simulation sickness symptoms and if necessary, help the participants in controlling the driving simulator. We always started the game using its fully immersive mode. However, since the participants of this study were from a vulnerable population, upon slightest symptoms of simulation sickness and. 26.

(39) discomfort, the HMD was removed, and the training session was continued in the non-immersive mode. If a participant could not even tolerate the non-immersive mode, s/he was withdrawn from the study.. 3.5. Data analysis We hypothesized: 1) Repeated practice with the driving simulator would improve the participants’ spatial cognition measured by the spatial learning score and the Morris water test outcomes. 2) The designed cognitive program would have a positive effect on the users’ mood measured by MADRS assessment score and, 3) Repeated exposure to the VRDS would not significantly impact the severity of the simulation sickness measured by SSQ questionnaire score. To investigate the first hypothesis, we used repeated measure multivariate analysis of variance (MANOVA) using R’s MANOVA.RM package [58], [59] followed by post-hoc analysis for each of the dependent variables of the Morris water test (time and normalized correct trajectory). Furthermore, for investigating the other hypotheses, we used paired t-test. In case the repeated measure MANOVA and paired t-test assumptions were not met, we used their equivalent nonparametric and distribution-free statistical tests. In all instances, a P-value < 0.05 was considered significant; in post-hoc analysis, the Bonferroni-correction was applied to consider a P-value < 0.025 as significant (2 dependent variables and 2 time points). All statistical analysis was conducted in R [58].. 3.6. Summary In this chapter of the thesis, the design process and the development of the VRDS was described. The virtual environment of the game including various natural and traffic elements was. 27.

(40) discussed. Also, the two phases of the VRDS, i.e. demo and training, were introduce. Furthermore, the conducted pilot study for evaluating the effects of the VRDS on the participants’ spatial cognition and mood was elaborated. Different assessments such as the VR Morris water test as the independent spatial assessment were introduced and the approach for evaluating their results was described. In the end, the conducted statistical analyses for investigating the hypotheses of the study were discussed.. Table 1- Maximum error for each level of the VRDS. 28. Maximum error of. Maximum error of. Maximum error of. level one. level two. level three. 4. 7. 11.

(41) Table 2- Participants’ demographic information (mean ± stdev.) Group. MoCA score. Age (yrs). Sex. Healthy (n=3). 26.7±1.2. 85±6. 1 Male. MCI (n=4). 22.8±1.5. 79±10.5. 1 Male. Alzheimer’s (n=4). 15.3±5.6. 69±7.1. 1 Male. 11persons. 21.1±5.9. 77±10.1. 3 Males. Special case. 2. 77. Male. 29.

(42) Chapter 4: Results Out of 11 participants enrolled in this study, data of one of the healthy participants (MoCA = 28) was excluded due to her eye sensitivity, i.e. she could not focus on the screen and finish the assessments. The results presented here are the average outcomes of the 10 remaining participants in the game. Out of the 10 participants, 2 were considered healthy, 4 participants were considered as MCI and 4 were considered as Alzheimer’s. We also report the progress of the special advanced Alzheimer’s participant in using the VRDS separately. The main spatial assessment for this study was the virtual replica of the Morris water test. None of the participants were able to pass the training trials of the VR Morris water test fully (finding the target before 45 sec.) neither at baseline nor at post-intervention. The repeated measure MANOVA on the time variable of the training trials and the normalized correct trajectory of the test trials, showed significant difference at post-intervention from baseline (P-value < 0.01). Both the total time and the normalized correct trajectory metrics increased from baseline to postintervention: the total time from 103.5±11.6 sec. to 105.2±11.0 sec. (not a significant increase) and the normalized correct trajectory from 0.58±0.14 to 0.84±0.07 (a significant improvement equal to 44.8% in the pos-hoc analysis and with P-value < 0.025). The results are shown in Figure 6 and Table 3. Although the number of participants in this pilot study were small, since their cognitive status differed significantly, we also calculated the above metrics in three subgroups of healthy, MCI and Alzheimer’s participants. Figure 7 shows the difference in the averaged normalized correct trajectory for the participants’ subgroups from baseline to post-intervention. As can be seen, the participants’ normalized correct trajectories improved at post-intervention noticeably; with a. 30.

(43) greater difference for the subgroups with higher MoCA. We did not run any statistical analysis on the subgroups’ data as the sample size in each subgroup was too small for a meaningful statistical analysis. Table 4 demonstrates the details of how many trials and at what difficulty level each participant played the VRDS over the two weeks period of the study. Similar to the evaluation of the VR Morris water test, to investigate the progress of the users in the game, alongside of evaluating the participants’ individual performance, we classified the users based on their cognitive status; calculated the arithmetic mean of the spatial learning score for each subgroup of the participants. Figure 8 shows the average spatial learning score plots of the three cognitive subgroups over the two-weeks period of the study. As can be seen, the spatial learning scores of all subgroups show an increasing trend (improvement) as expected. Figure 9 shows the spatial learning score of the advanced Alzheimer’s participant, who was enrolled in the study as a special case. His spatial learning scores also have improved throughout the training sessions. For the spatial learning score plots of all participants, please refer to the Appendix A. From our participants, only 3 of them (2 healthy and 1 participant with Alzheimer’s) were able to use the fully immersive mode of the VRDS. Other participants were not able to continue with the immersive mode as they were experiencing dizziness, i.e. a disorientation symptom of the simulation sickness syndrome. SSQ assessment and its three subcategories, oculomotor, nausea and disorientation symptoms, were analyzed at baseline and post-intervention. All the subcategories of the assessment and also its total mark showed no significant difference between baseline and post-intervention (Table 3).. 31.

(44) The participants’ MADRS score comparison analysis at post-intervention with respect to baseline, showed 14.3% improvement (from 2.1±0.6 at baseline to 1.8±0.4 at post-intervention); this difference was not statistically significant (Table 5). However, if we exclude the participants with MADRS score equal to zero at baseline (n=2, ceiling effect), the improvement becomes 23.1% (from 2.6±0.6 at baseline to 2.0±0.5 at post-intervention, however still not significant).. 32.

(45) Table 3- Descriptive statistics (mean ± SE) of the evaluated variables, (MWT: Morris water test), *: Significant difference (p < 0.025). Descriptive Statistics. MWT, Training Trials. MWT, Testing Trials. (n=10). (n=10). Total Time. Normalized. SSQ (n=10). Nausea. Oculomotor. Disorientation. Total. Correct Trajectory. Baseline. 103.5±11.6. 0.58±0.14. 7.6±4. 7.6±3.2. 4.2±3. 7.9±3.1. Post-intervention. 105.2±11.0. 0.84±0.07*. 8.6±3.3. 4.7±1.7. 9.7±5.5. 8.1±3.1. 33.

(46) Table 4- Participants’ descriptive information on using the VDS, MCI: Mild Cognitive Impairment, AD: Alzheimer’s disease Participant Condition. Total number. Number of. Number of. Number of. of trials. trials - Level 1. trials- Level 2. trials - Level 3. 1. Healthy. 26. 6. 8. 12. 2. Healthy. 23. 4. 7. 12. 3. MCI. 20. 4. 9. 7. 4. MCI. 21. 9. 8. 4. 5. MCI. 26. 5. 13. 8. 6. MCI. 43. 12. 21. 10. 7. AD. 27. 14. 11. 2. 8. AD. 26. 12. 14. 0. 9. AD. 17. 8. 7. 2. 10. AD. 24. 12. 12. 0. 25.3±7.0. 8.6±3.7. 11.0±4.3. 5.7±4.7. 19. 17. 2. 0. Average ± stdev. 11. Advanced AD. -. 34.

(47) Table 5- Participants’ MADRS scores at baseline and post-intervention assessment sessions, MADRS: Montgomery-Asberg Depression Scale, MCI: Mild Cognitive Impairment, AD: Alzheimer’s disease Participant. Condition. 1. Healthy. 2. Healthy. 3. MCI. 4. MCI. 5. MCI. 6. MCI. 7. AD. 8. AD. 9. AD. 10. AD Average ± stdev.. MADRS score. MADRS score. Baseline. Post-intervention. 1. 1. 6. 2. 2. 3. 2. 0. 2. 2. 0. 1. 0. 1. 1. 5. 4. 2. 3. 1. 2.1±1.9. 1.8±1.4. 35.

(48) Figure 6- VR Morris water test, performance metrices. Normalized correct trajectory, Testing trials. 36. Time, Training trials.

(49) Figure 7- Difference in the normalized correct trajectory from baseline to post-intervention, testing trials of the Morris water test (mean ± SE), Healthy participants (n=2), MCI participants (n=4) and Alzheimer’s participants (n=4) 0.6 0.5 0.4 0.3 0.2 0.1 0. 0.31. 0.29. 0.2. Healthy. MCI. AD. 37.

(50) Figure 8- Spatial learning score plots of the participants’ subgroups. The red dot refers to the optimum learning: marked spatial learning score is 91% of the maximum spatial learning score for the 3rd level.. 3.00 2.50 2.73. 2.00 1.50 1.00 0.50 0.00 1. 3. 5. 7. 9 11 13 15 17 19 21 23 25. Trials. a) Healthy participants (n=2), optimum learning: red. 38. Spatial Learning Score. Spatial learning score. 3. R² = 0.4956. 2.5 2 1.5 1 0.5 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27. Trials. b) MCI participants (n=4). R² = 0.4764. 2.5. Spatial Learning Score. R² = 0.683. 3.50. 2 1.5 1 0.5 0 1. 3. 5. 7. 9 11 13 15 17 19 21 23 25. Trials. c) Alzheimer’s participants (n=4).

(51) Figure 9- Spatial learning score plot, special case of the study. The red dot refers to the marked improvement: marked spatial learning score is 75% of the maximum spatial learning score for the 1st level. Spatial Learning Score Plot, Advanced AD Participants (MoCA = 2), Maximum level =2. Spatial Learning Score. 1.2 1 0.8 0.75. 0.6 0.4 0.2 0 1. 2. 3. 4. 5. 6. 7. 8. 9. 10 11 12 13 14 15 16 17 18 19. Trials. 39.

(52) Chapter Five: Discussion Previous research has demonstrated that computer based cognitive training programs can be beneficial for the population with MCI and dementia. These programs have had significant positive effects on the MCI population’s general cognition, verbal learning, verbal memory and working memory. Visuospatial memory, executive functioning and attention have been reported to show improvement although not extensively by some cognitive training [60]. The overall effect of conducting computer training programs for dementia participants has been shown to be positive, with significant positive outcomes on participants’ spatial cognition [60]. In a case study, outcomes of a VR spatial training program were evaluated for a participant at the onset of Alzheimer’s [6]. The study demonstrated the training program’s promising effects on the participant’s VR navigation and real-world navigation. The participant was not only able to complete the navigational tasks in VR environment with zero error after 8 weeks of training, but also, he was able to start driving independently [6]. The results of our independent assessment (the VR Morris water test) to investigate the far effects of our training program, show significant improvement from baseline to post-intervention for the normalized correct trajectory (Figure 7). On the other hand, none of the participants were able to find the target within the cut-off duration of the assessment’s training trials (45 sec.). This may imply that, although the Morris water test is a challenging assessment for older cognitively impaired participants, they are still able to demonstrate improved performance in the test. In previous studies, it was shown healthy (control) participants were able to exploit distal cues more efficiently than dementia participants or participants with hippocampus anatomical damage, to travel a direct path towards the target in the Morris water test [55], [56]. In another. 40.

(53) study, younger adults in comparison to healthy seniors had more cross overs with the target and spent most of their traversed trajectory in the quadrant of the target i.e. higher (normalized) correct trajectory [57]. Congruent with those studies, the participants of our study spent a larger portion of their traversed trajectory in close approximation with the target after the two-week training with the VRDS; they had higher normalized correct trajectory in post-intervention in comparison to baseline. The near effect of our training program can be observed by the upward trend of spatial learning scores during the training sessions. As expected and congruent with previous research [61], the healthy group learned better than MCI group, and also the MCI group learned better than Alzheimer’s group (Figure 8). To investigate the learning effect further, we define an optimum learning for each level, and investigate which subgroup of participants reached that optimum learning. We say a subgroup of participants, has achieved the optimum learning when they could achieve a learning score of at least, 80% of the level’s maximum spatial score, and maintain that learning score for at least three consecutive trials of the game at the same level. In the spatial learning score plots, the optimum learning points (if it was reached) are demonstrated by a red mark on the Figure 8. As observed from this figure, only healthy subgroup of participants achieved the optimum learning (Figure 8a). This implies MCI and Alzheimer’s participants might have needed a longer training period to reach a marked plateau in their learning score plots. The special case of the study, the participant at the advanced stage of the Alzheimer’s, showed a plateau equal to 75% and for the first level of the VRDS (Figure 9). Although he did not achieve the optimum learning, he had an improvement roughly equal to the optimum learning. This implies even an advanced Alzheimer’s participant may improve by training but perhaps needs much longer training period. Thus, the two-weeks duration of our training program is probably 41.

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