Wearable sensor system for human localization and motion capture

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(1)Wearable Sensor System for Human Localization and Motion Capture by Shaghayegh Zihajehzadeh M.Sc., Amirkabir University of Technology, 2011 B.Sc., Isfahan University of Technology, 2008. Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. in the School of Mechatronic Systems Engineering Faculty of Applied Sciences.  Shaghayegh Zihajehzadeh 2017 SIMON FRASER UNIVERSITY Spring 2017.

(2) Approval Name:. Shaghayegh Zihajehzadeh. Degree:. Doctor of Philosophy. Title:. Wearable Sensor System for Human Localization and Motion Capture. Examining Committee:. Chair: Gary Wang Professor. Edward J. Park Senior Supervisor Professor Stephen Robinovitch Supervisor Professor Kevin Oldknow Supervisor Senior Lecturer Carolyn Sparrey Internal Examiner Associate Professor Benny Lo External Examiner Lecturer Department of Surgery and Cancer Imperial College London. Date Defended/Approved: April 27, 2017. ii.

(3) Ethics Statement. iii.

(4) Abstract Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturing process of MEMS sensors, existing wearable MoCap systems suffer from drift error and accuracy degradation over time caused by time-varying bias. The goal of this research is to develop algorithms based on multi-sensor fusion and machine learning techniques for precise tracking of human motion and location using wearable inertial sensors integrated with absolute localization technologies. The main focus of this research is on true ambulatory applications in active sports (e.g., skiing) and entertainment (e.g., gaming and filmmaking), and health-status monitoring. For active sports and entertainment applications, a novel sensor fusion algorithm is developed to fuse inertial data with magnetic field information and provide drift-free estimation of human body segment orientation. This concept is further extended to provide ubiquitous indoor/outdoor localization by fusing wearable inertial/magnetic sensors with global navigation satellite system (GNSS), barometric pressure sensor and ultra-wideband (UWB) localization technology. For health applications, this research is focused on longitudinal tracking of walking speed as a fundamental indicator of human well-being. A regression model is developed to map inertial information from a single waist or ankleworn sensor to walking speed. This approach is further developed to estimate walking speed using a wrist-worn device (e.g., a smartwatch) by extracting the arm swing motion intensity and frequency by combining sensor fusion and principal component analysis.. Keywords:. inertial/magnetic sensor; orientation estimation; position estimation; walking speed; Kalman filter; Gaussian process regression.. iv.

(5) Dedication I dedicate this work to my caring husband and devoted parents.. v.

(6) Acknowledgements I would like to thank my senior supervisor, Dr. Edward Park, for providing invaluable mentorship, technical expertise and encouragement throughout my PhD studies. His support, inspiration, professional guidance and patience helped me enjoy every moment of working on this thesis. I would also like to thank my supervisory committee: Dr. Stephen Robinovitch and Dr. Kevin Oldknow for providing expert opinion and evaluating different parts of this research. I am also thankful to Dr. Farid Golnaraghi for his confidence in me and accepting me to this PhD program and guiding me during the first few months of my studies. I would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support through Vanier Canada Graduate Scholarship (CGS) program. I am grateful to my lab colleagues and friends: Dr. Omar Aziz, Dr. Ahmed Arafa, Darrell Loh, Matthew Lee, Paul Yoon, Magnus Musngi, Dr. Majid Shokoufi, Shervin Jannesar, and Behzad Abdi. I am glad I had the opportunity to meet and spend time with these amazing people. Many thanks go to my lovely friends Nastaran Hajinazar, Amir Pourmand and Rajesh Rao for their unconditional friendship and support, making my life more joyful. Family isn’t always blood; you are my family in Canada. My special thanks go to my caring husband, Ramin who motivated me in so many ways. There are no words that can express my gratitude and appreciation for all you have done and been for me. Your continued and unfailing love, support and understanding made the completion of this thesis possible. I am thoroughly thankful to my parents and parents-in-law for their encouragement and unwavering belief in me.. vi.

(7) Table of Contents Approval .......................................................................................................................... ii Ethics Statement ............................................................................................................ iii Abstract .......................................................................................................................... iv Dedication ....................................................................................................................... v Acknowledgements ........................................................................................................ vi Table of Contents .......................................................................................................... vii List of Acronyms............................................................................................................. ix Chapter 1. Introduction ............................................................................................. 1 1.1. Background and Motivation .................................................................................... 1 1.2. Objectives............................................................................................................... 2 1.3. Organization of the Dissertation .............................................................................. 3 Chapter 2. Literature Review ..................................................................................... 5 2.1. MoCap of human body segments ........................................................................... 5 2.2. Indoor and outdoor localization ............................................................................... 7 2.3. Walking speed estimation using wearable IMU ..................................................... 10 Chapter 3. Summary of Contributions.................................................................... 13 3.1. Estimation of Human Body Segment Orientation .................................................. 13 3.1.1. A cascaded two-step KF for estimation of human body segment orientation using wearable IMU ............................................................... 13 3.2. Tracking of Position and Velocity Trajectories ...................................................... 14 3.2.1. Integration of MEMS inertial and pressure sensors for vertical trajectory determination ........................................................................... 14 3.2.2. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications ................................................................................... 15 3.2.3. UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking.................................................................. 16 3.3. Magnetometer-free Motion Tracking ..................................................................... 18 3.3.1. A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion ......................................................................... 18 3.3.2. A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture ........................................ 19 3.4. Regression Model-based Walking Speed Estimation ............................................ 20 3.4.1. Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU .............................................. 20 3.4.2. Regression model-based walking speed estimation using wristworn inertial sensor ................................................................................. 21 Chapter 4. Conclusions and Future Work .............................................................. 23 4.1. Conclusions and Contributions ............................................................................. 23 vii.

(8) 4.2. Future Work.......................................................................................................... 25 References ................................................................................................................ 27 Appendix A. A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU ......................................................... 35 Appendix B. Integration of MEMS inertial and pressure sensors for vertical trajectory determination ........................................................................................ 40 Appendix C. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications ........................................................................................... 52 Appendix D. UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking ................................................................ 64 Appendix E. A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion .......................................................................... 76 Appendix F. A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture ..................................................... 81 Appendix G. Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU................................................. 94 Appendix H. Regression model-based walking speed estimation using wrist-worn inertial sensor ...................................................................................... 99. viii.

(9) List of Acronyms EKF. Extended Kalman Filter. FD. Frequency Domain. G-N. Gauss Newton. GNSS. Global Navigation Satellite System. GPR. Gaussian Process Regression. GPS. Global Positioning System. IMU. Inertial Measurement Unit. INS. Inertial Navigation System. KF. Kalman Filter. KPVs. Key Performance Variables. MEMS. Micro-Electro-Mechanical Systems. MoCap. Motion Capture. NLOS. Non-Line-of-Sight. O2OQ. Optimal Two-Observation Quaternion Estimation. PCA. Principal Component Analysis. PIR. Passive Infrared. RFID. Radio Frequency Identification. RTS. Rauch-Tung-Striebel. SVR. Support Vector Regression. TD. Time Domain. TOA. Time of Arrival. TUG. Timed-Up-and-Go. UKF. Unscented Kalman Filter. UWB. Ultra-Wideband. ZUPT. Zero Velocity Update. ix.

(10) Chapter 1. Introduction 1.1. Background and Motivation Simultaneous localization and motion capture (MoCap) of human body segments is important in numerous applications such as sports science [1]-[2], rehabilitation [3] and health monitoring [4]. For instance, in sports, access to quantitative key performance variables (KPVs) can significantly improve overall performance of the athletes [5]-[6]. In rehabilitation, quantitative evaluation of body segment motion in patients with neurological disorders who suffer from dysfunction of the limbs can expedite their recovery process through customized exercise plans [7]. In health monitoring, walking speed can be used as a precursor to predict mild cognitive impairment [8], multiple sclerosis [9], Parkinson’s disease [10], chronic obstructive pulmonary disease [10] and risk of falls [11]. Currently available video-based or camerabased MoCap approaches provide few quantitative variables [12]. In addition to being costly, they are relatively complex, require careful setup, and are limited to confined areas. Inertial navigation systems (INS) based on inertial measurement units (IMU), on the other hand, are self-contained and thus can provide unconstrained accessibility to advanced motion and location information for truly ‘ambulatory’ applications. Since its emergence, INS is widely used in various MoCap applications such as ships [13] and aircraft [14]-[15] navigation, fastening tool [16] and pen [17] tracking, in addition to sport analysis [18] to provide navigation information such as attitude, velocity, and position. Recently, with the advances in MEMS technology, MEMS-IMU (typically consisting of a tri-axial gyroscope, a tri-axial accelerometer and a tri-axial magnetometer) has emerged as a wearable MoCap device for human motion tracking in various sports and gaming 1.

(11) applications. Additionally, in health status monitoring applications, the advent of affordable wearable IMU facilitated considerable research to estimate walking speed in an ambulatory fashion [20]. However, due to manufacturing process, the wearable IMU suffer from inherent errors such as bias and scale factor instability which in turn results in accumulated drift error over time when employed for human MoCap [19]. As a result, signal processing techniques including sensor fusion and machine learning have emerged to improve the IMU-based human MoCap. For example, fusion of IMU with global navigation satellite system (GNSS) for outdoor environment and ultra-wideband (UWB) for indoor navigation has received an increased attention recently. Likewise, regression methods such as Gaussian process regression (GPR) have emerged as a preferred methodology for ambulatory walking speed estimation based on a set of inertial features [20]. This Ph.D. thesis works toward alleviating the shortcoming of wearable IMU for ambulatory human motion analysis. The focus of this research is on novel sensor fusion and pattern recognition algorithms based on inertial sensors and absolute localizations technologies to develop a 3-D motion tracking system for true ambulatory applications, specifically aimed at active sports (e.g., skiing), entertainment (e.g., gaming) and health monitoring. In sports and gaming application, this research will bring ambulatory MoCap system to perform realistic MoCap in large indoor/outdoor areas such as soccer fields and ski hills, opening up new opportunities in the area of high performance sports training. For health monitoring applications, this research will provide a basis for longitudinal analysis of human walking speed, which can be used to as an important biomarker to study health status.. 1.2. Objectives The aim of this thesis and my future research is the development of practical, reliable and commercializable algorithms and methods for human motion analysis. The objectives of this dissertation are to: . To reconstruct 3-D human lower body motion based on segments’ inertial information: An advanced Kalman filter (KF) based fusion algorithm will be 2.

(12) developed to fuse inertial and magnetic information from the IMUs attached to the 7 rigid lower-body segments and estimate the orientation of each body segment. Since human body segments can be treated as a system of rigid links connected by joints, 3-D lower body motion can be reconstructed by knowing the orientation of each body segment. . To develop an indoor/outdoor localization system: In typical MoCap applications, position tracking which gives access to quantitative KPVs is of key importance. In order to accurately track position of human subjects, INS based on wearable IMU will be developed. This INS will be aided with UWB localization mainly for indoor tracking and GNSS signal for outdoor tracking applications.. . To develop a magnetometer-free localization and lower-body MoCap system: The presence of electromagnetic disturbances in indoor environment affects the accuracy of yaw angle estimation in wearable MoCap system based on IMU. Kinematic constraints of the human body (joints), UWB position measurements from multiple nodes on the body and inertial data from accelerometer and gyroscope will be fused to provide a magnetometer-free estimation of yaw angle and 3-D lower-body motion.. . To develop a walking speed estimation algorithm based on a single bodyworn IMU: A regression model will be developed to map the inertial information from ankle, waist and wrist-worn IMU to walking speed. Various inertial variables and feature sets will be studied to develop an accurate walking speed estimation model for each IMU mounting locations.. 1.3. Organization of the Thesis This thesis is comprised of four chapters which are organized as follows: Chapter 1 provides background and motivation behind the present work. In Chapter 2, a comprehensive literature review is provided on three major aspects of this thesis: (i) systems and methods for capturing motion of human body segments, (ii) human localization in indoor and outdoor settings, and (iii) accurate tracking of walking speed using wearable IMU. Chapter 3 provides a summary of the main contributions of the thesis. The novel sensor fusion algorithm and its capability for estimating human body segment orientation using a wearable IMU is presented in Sec. 3.1. In Sec. 3.2, the proposed system and method for tracking position and velocity trajectories in indoor and outdoor settings is explained. Sec. 3.3 shows how a biomechanical model of the human body can be used 3.

(13) along with the proposed algorithms in Sec. 3.1 and 3.2 to develop a magnetometer-free lower-body MoCap system, which solves one of the most important challenges in indoor inertial MoCap – the adverse effects of magnetic disturbances. Finally, in Sec. 3.4, the proposed walking estimation method based on a single body-worn IMU is described. Chapter 4 provides conclusions and future works that may stem from this work.. 4.

(14) Chapter 2. Literature Review In this chapter, a comprehensive literature review is presented on the following three major aspects of this thesis: MoCap of human body segments, human localization in indoor and outdoor settings, and accurate tracking of walking speed using a wearable IMU.. 2.1. MoCap of human body segments There are a large number of publications on tracking human body segment movement. This section provides a thorough literature survey on the available MoCap devices in addition to some of the conventional and relevant inertial tracking algorithms. The traditional MoCap devices include optical, magnetic and mechanical systems. The optical MoCap devices are highly accurate, but they are expensive, require clear line of sight and careful setup, and they are limited in range such that their application is restricted to confined areas such as MoCap studios [22]. The magnetic systems are relatively low cost but, similar to optical devices, they are limited in range [23]. They can also be affected by ferromagnetic materials and other sources of magnetic field distortion in surrounding environment, especially in indoors. Mechanical sensors can provide very accurate estimation of body segment orientation free of occlusion but they can be cumbersome to use for monitoring daily activities [23]. Considering the inherent limitations of the above mentioned traditional MoCap devices, recently, the availability of low-cost wearable IMU that can be used to track human movement in and outside of a laboratory, has provided an alternative means to overcome the limitations of other MoCap systems [24]. 5.

(15) Using an IMU, 3-D orientation can be obtained by integrating the angular velocities from the tri-axial gyroscope, but this causes unbounded orientation drift due to the gyroscope’s bias instability [2]. In order to compensate for this error, accelerometer and magnetometer are employed as the vertical (the gravity) and horizontal (the Earth’s magnetic field) references, respectively [25]. Several methods have been used so far to estimate orientation using inertial and magnetic data. Luinge et al. showed that orientation obtained by integrating gyroscope’s rotational rate can be improved by fusing inclination information obtained from accelerometers [26]-[27]. In their system, the tilt angles (orientation around the vertical axis) were obtained by integrating angular rate and gravitational acceleration was used to correct drift. The difference between gyroscope and accelerometer tilt was used as an input to a KF to obtain a more accurate tilt angles [27]. However, the experimental results show that the heading (yaw) angle can still drift over time [27] and the main reason is due to lacking magnetometer. Roetenberg et al. fused magnetometer with gyroscope and accelerometer in a complementary KF [28]. They introduced a KF that operates on two inputs to estimate 3-D orientation of human body segments. The first input is the difference between inclination obtained from accelerometer and gyroscope. The second input is the difference between the magnetic field measurements from magnetometer and the one that gyroscope estimates. They included gyroscope bias error and orientation error as part of the process model. They also included magnetic disturbances in their model and tried to estimate those disturbances. The filter was tested under static and dynamic conditions with ferromagnetic materials close to the sensor and their results show accurate and drift-free orientation estimates. However, the authors reported that the tracker was tested under controlled and limited conditions, and the accuracy of orientation angle estimates could change with faster and more complex movements. In another study, Roetenberg et al. fused a magnetic system with inertial sensors in which the magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source [29]. Their system shows good position and orientation tracking. 6.

(16) accuracies, but the errors were expected to grow if ferromagnetic materials were anywhere close to the magnetic system [29]. More recently, Lee et al. introduced new fusion methods to improve the computational cost of the available algorithms for orientation estimation [25]-[30]. In [25], they used optimal two-observation quaternion estimation method [31] (referred to as O2OQ) in addition to a separate vector selector algorithm in a linear KF algorithm which has the minimum number of states to estimate the full 3-D orientation. The results show that their computationally efficient algorithm will improve the accuracy of the estimated orientation under dynamic conditions and/or magnetic disturbances. In [30], they proposed a fast quaternion-based orientation algorithm which uses conventional GaussNewton (G-N) optimization method to remove the orientation drift caused by integration of gyroscope signal. In another subsequent study, they proposed a KF-based algorithm which fuses accelerometer and gyroscope to estimate the tilt angles (i.e. roll and pitch angles) [32]. The proposed algorithm uses an acceleration model-based approach to deal with the estimation problem during dynamic condition. The experimental results show that their proposed algorithm can accurately estimate tilt angles and external accelerations for short accelerated periods; however, for prolonged high external accelerations, the proposed algorithm exhibits gradually increasing errors [32].. 2.2. Indoor and outdoor localization As mentioned earlier, INS based on wearable IMU present a great potential for the purpose of human localization [33]-[34]. However, unlike the tactical grade IMU, these wearable devices suffer from manufacturing imperfections which cause some changes in their characteristics such as bias and scale factor with the change of environmental conditions. Therefore, the inertial navigation solution based on wearable IMU will drift quickly due to the integration of bias over time. The answer to this problem may involve the fusion of an absolute localization system with IMU. This combination has become increasingly popular given certain complimentary characteristics [35]. The absolute localization systems are stable over long periods of time; however, because of the line of sight requirement, their navigation performance can suffer from short-term outages due to occlusion. Conversely, INS is reliable over short time periods, but lacks 7.

(17) long-term stability due to the accumulation of sensor errors. By combining these two technologies, better accuracy is obtained than if either technology is used in isolation. The remainder of this section will provide a literature survey on the available localization systems for indoor and outdoor motion tracking applications in addition to the pertinent fusion algorithms [36]. Waegli et al. compared the performance of two fusion methods for the integration of IMU with low cost global positioning system (GPS) [12] for the purpose of trajectory tracking in sports. These two fusion methods are loosely coupled and tightly coupled algorithms, respectively, based on extended Kalman filter (EKF). In the loosely coupled approach, GPS coordinates and velocities are fed to the filter as measurement updates while in the tightly coupled approach, raw GPS signals (carrier-phase smoothed pseudoranges and Doppler measurements) are fed to the EKF at the update stage. Post processing Rauch-Tung-Striebel (RTS) [37]-[38] smoothing algorithm has also been used for further error reduction. By comparing the estimated trajectory with the one from the reference system consisting of a tactical-grade IMU and a dual frequency GPS receiver, they concluded that the loosely coupled integration strategy provides slightly increased performance over the tightly coupled approach for full or partial satellite constellations [12]. They have also compared the performance of unscented Kalman filter (UKF) with EKF for the same purpose and they found similar accuracies for both methods in sport application but higher computational cost for UKF, which makes it comparatively less interesting [39]. Additionally, Brodie et al. used a combination of GPS, IMU and pressure sensitive insole sensors in a fusion algorithm to capture 3-D kinetics and kinematics of alpine ski racing [40]. More recently, Sadi et al. combined three linear Kalman filters with a complementary EKF to improve the performance of the conventional GPS/IMU fusion based on EKF [5]. They have shown that their method can accurately calculate jump KPVs (key performance variables) with the accuracy of less than 14 cm. One issue in the above mentioned GPS/IMU fusion approaches is that the consumer grade GPS-derived vertical positional (or altitude) information is much less accurate than the horizontal position [41]. According to [42], GPS altitude measurement accuracies can vary up to 40 m. One potential way of increasing vertical position. 8.

(18) information is by using a barometric pressure sensor, which can also derive altitude using barometric pressure measurements. However, height measurements from MEMS barometric pressure sensors suffer from thermal noise and a significant amount of quantization noise [43]. This makes them unsuitable for tracking highly dynamic movements in sports, but may still be used to stabilize the drift-prone IMU for navigation in the vertical direction [43]. The integration of a MEMS barometric pressure sensor with an IMU for applications under dynamics typical for human movements is investigated in [43]-[44]. In addition to outdoor localization, fusion of IMU and an absolute localization system has been used for indoor navigation. Fusion of monocular camera as a vision sensor with a low cost IMU in a vision-aided INS has been thoroughly investigated by Panahandeh et al. in [45]. Another vision-based fused localization system for human tracking based on inertial sensors and wearable monocular camera is introduced in [46]. An indoor tracking system based on inertial sensors, radio frequency (RF) and ultrasound beacons is introduced in [47] and another one based on radio frequency identification (RFID) technology and inertial sensors is proposed in [48]. Among the available radio positioning systems, UWB technology is another promising technology for localization [49]. The original distinguishing feature for the UWB radios is their potential ability to transmit in an unlicensed way with very low power over an ultra-wide portion of the spectrum, allowing this technology to coexist with current and future licensed wireless systems [50]. In fact, UWB technology makes use of sub-nanosecond duration pulses with several GHz of bandwidth which offers the unique possibility of distinguishing the different multipath components, accurate estimation of time of arrival (TOA), and finally accurate estimation of position [50]. In contrast to these advantages, UWB system suffers from non-line-of-sight (NLOS) conditions and often multipath effects which in turn affect the final positioning accuracy. That is why the integration of UWB with IMU has attracted many researchers. Pittet et al. were among the pioneers who investigated the integration of IMU/UWB for the purpose of indoor human tracking and they reported an accuracy of. 9.

(19) about 1m [51]. Hol et al. introduced a tightly coupled fusion algorithm based on EKF to fuse UWB measurements with IMU [52]. This algorithm estimates position as well as orientation of the sensor unit while being reliable in case of multipath effects and NLOS conditions. Corrales et al. investigated the performance of loosely coupled fusion algorithms based on a linear KF, a particle filter and a combination of both for indoor human tracking using UWB/IMU fusion [53]. Their experimental results show that the combined Kalman/particle filter is slightly more accurate that a standalone KF. However, the computational cost of the combined filter is considerably higher than that of the linear KF.. 2.3. Walking speed estimation using wearable IMU Walking speed and its variation over time can be considered as a powerful predictor of hospitalization, disability and survival [54]-[55]. In a clinical setting, different protocols including the 4-meter [56], 10-meter [57], 6-minute walking tests [20] and the timed up and go (TUG) test [58]-[59] have been used as standard tools to evaluate walking speed and gait parameters. However, the short walking tests (e.g., the 10-meter walking test) are subject to bias due to their brevity [60] and the longer tests are less accepted due to the space and time constraints in clinical exams [61]. Additionally, walking speed results of the clinical tests cannot be fully applied to free-living environment [62]. This emphasizes the need for a reliable system/method for longitudinal (and continuous) walking speed measurement in real-world situations. Aiming at longitudinal walking speed measurement outside the clinical setting, some researchers have used passive infrared (PIR) motion sensors. These PIR sensors can be mounted on ceiling [63] or walls [64] of a residence and can measure the individuals’ walking speed when they are in the field of view of the sensors. However, walking speed measurement based on PIR sensors is limited to confined areas such as hallways. Additionally, such system cannot differentiate between multiple residents, limiting its application to independent-living resident homes. Camera-based systems have also been used in the literature for in-home gait speed measurement [65]. However, camera-based systems can get affected by the lighting conditions and similar. 10.

(20) to the PIR sensors, and they are limited to confined areas and hence more suitable for the clinical settings. Fortunately, with the recent advances in the MEMS sensor technology, wearable IMU can enable walking speed measurement in an ambulatory fashion. Considering that the acceleration data from tri-axial accelerometer in a wearable inertial sensor can be integrated to estimate the velocity, Laudanski et al. have proposed an integration-based approach for speed tracking [21]. However, the main challenge in the integration-based approach is the velocity drift over time that happens as a result of time-varying bias in MEMS-based IMU [66]. To mitigate the drift, Laudanski et al. have proposed the detection of periodic foot stance phases during walking to reset the velocity to zero through a process called zero velocity update (ZUPT) [21],[66]-[69]. However, the need for foot-stance detection requires the wearable sensor to be normally mounted on the leg (ideally on the foot), which is inconvenient for longitudinal walking speed monitoring. Using waist-worn IMU, Hu et al. have modeled the foot swing in walking as an inverted pendulum in order to find a 3-D walking kinematic model for speed estimation [70]. On the other hand, regression models have emerged as a preferred methodology for walking speed estimation based on a set of inertial features. A support vector regression (SVR) model based on acceleration data and a Gaussian process regression (GPR) model based on acceleration and angular velocity data from a waist-mounted IMU are proposed by Schimpl et al. [20] and Vathsangam et al. [71], respectively, to estimate average walking speed. These regression-based approaches for walking speed estimation are based on mapping the inherent pattern of acceleration and rate of turn information corresponding to the hip rotation in a gait cycle to walking speed. Accuracy of the regression algorithms depends on the set of variables and the extracted features. In inertial walking speed estimation, the variables can be divided into raw variables (the signals collected directly from the inertial sensors) and the processed variables (the signals obtained by manipulation of raw variables using filtering techniques such as sensor fusion). For instance, the external (gravity compensated) acceleration is a processed variable obtained from acceleration and angular velocity using a KF fusion algorithm [2]. Furthermore, the features can be generally divided into. 11.

(21) time-domain and frequency-domain features. Due to the periodicity of walking, frequency-domain features of raw inertial variables from a waist-mounted IMU have been extensively used for walking speed estimation [71]. On the other hand, conventional time-domain features (such as minimum, maximum, sum of absolute values, etc.) have also been used in some other studies for the same raw variables in walking speed estimation [20].. 12.

(22) Chapter 3. Summary of Contributions The main contributions of this Ph.D. thesis are published in five peer-reviewed journal papers and three peer-reviewed conference papers enclosed in Appendices A-H. In this chapter, a summary of each paper is provided followed by a description of the key connections between the papers.. 3.1. Estimation of Human Body Segment Orientation 3.1.1.. A cascaded two-step KF for estimation of human body segment orientation using wearable IMU In many ambulatory biomechanical analyses, motion tracking of human body. segments by accurate determination of each segment’s orientation is of key importance [30]-[72]. The wearable miniature IMU, together with a sensor fusion algorithm, can be used to estimate accurate orientation of human body segments [25],[30],[73]. To estimate the full 3-D orientation (roll, pitch, and yaw), most researchers have focused on fusing data from the tri-axial accelerometer, gyroscope and magnetometer triplets in a KF together with an optimization algorithm such as O2OQ [25] and G-N [30]. These optimization methods provide the optimal orientation from accelerometer and magnetometer output vector for measurement update step in the KF. In spite of their accuracy, these optimization algorithms have high computational costs. This work introduces a novel fast two-step cascaded KF for orientation estimation without using optimization. The proposed algorithm uses two linear Kalman filters, consisting of a tilt angle (roll and pitch) KF followed by a yaw (heading) angle KF. The first step uses the accelerometer’s output vector along with an acceleration model to 13.

(23) accurately estimate the tilt angles. The second step extends the tilt angle algorithm to full 3-D orientation by a novel yaw angle estimation method. Using this proposed method, the effect of ferromagnetic disturbances is completely decoupled from the tilt angle estimation. Furthermore, the estimated tilt angles in the first step help to determine the yaw angle in the second step more accurately. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions. This orientation tracing method is a building block in the estimation of the position and velocity tracking trajectories described in Sec. 3.2. For additional information, the reader is referred to Appendix A or [2].. 3.2. Tracking of Position and Velocity Trajectories 3.2.1.. Integration of MEMS inertial and pressure sensors for vertical trajectory determination INS based on wearable IMU can provide athletes with KPVs allowing them to. share quantitative information with coaches, record performance over time, and even get real-time feedback of how they are doing for further improvement. Typically, these systems make use of an IMU and/or an absolute position sensor to capture motion in addition to localization. For outdoor localization, GPS is integrated with the inertial devices [74]. However, poor accuracy (10~20m) of the consumer grade GPS-derived vertical position, directly affects the altitude estimation based on the GPS/IMU integration. Although recent progress in real-time GPS technologies (real-time kinematic GPS or differential GPS) provide higher positional accuracies [75], their prohibitive cost is a limiting factor for the sport consumer electronics market. For the purpose of accurately determining vertical trajectories in sports, the performance of the wearable IMU integrated with the MEMS barometric pressure sensor is investigated in this work [1]. A cascaded two-step KF consisting of separate orientation and position/velocity subsystems is proposed for this integration. The twostep cascaded KF uses two linear KFs: an orientation KF for determination of tilt angles 14.

(24) followed by a vertical position/velocity KF. The proposed algorithm avoids the use of a magnetometer for attitude determination which makes it robust against electromagnetic disturbances. Additionally, due to its cascaded structure, it avoids the need to propagate additional states, resulting in more computational efficiency needed for small and lightweight battery-powered wearable technologies for sports. Slow human movements in addition to more rapid sport activities such as vertical and step-down jumps can be tracked using the proposed algorithm. The height tracking performance is benchmarked against a reference camera-based motion tracking system and an error analysis is performed. The experimental results show that the vertical trajectory tracking error is less than 28.1 cm. For the determination of jump vertical height/drop, the proposed algorithm has an error of less than 5.8 cm. For additional information, the reader is referred to Appendix B or [1].. 3.2.2.. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications In addition to vertical trajectory tracking explained in Sec. 3.2.1, horizontal. position and velocity trajectories are important parameters for quantitative performance analysis of athletes. Although these parameters can be obtained by GPS, the GPS sampling rate is typically too low to detect complex kinematic motions in sports competitions. Additionally, some other factors such as satellite signal attenuation in athletes' environment, and the number of satellites and their positions may also affect the accuracy of GPS position/velocity calculation [76]. Alternatively, an IMU can be integrated with GPS to detect rapid movements and bridge the gap between GPS blockage periods [36]. EKF and UKF are widely used for GPS/IMU integration in sport trajectory determination. However, despite their excellent sensor fusion capabilities in terms of accuracy, the above mentioned nonlinear Kalman filtering methods demand relatively high computational time that is not desirable in low-cost, battery-powered, and lightweight wearable/portable navigation systems worn by athletes. In addition to high computational cost, the available GPS/IMU KF-based fusion approaches rely on GPS observations to correct the otherwise drift prone orientation calculated by the gyroscope [77]. They make use of the fact that errors in the attitude solution of an INS propagate into. 15.

(25) errors in velocity. Therefore, errors in attitude can be observed through as independent measurement of velocity under particular motion conditions that include acceleration, which is non-parallel to the velocity vector in the navigation frame [77]. However, the accuracy and speed of attitude correction by this method depends on frequency and magnitude of acceleration maneuvers [67]. Additionally, using GPS position and velocity observations to correct orientation is problematic for long GPS outage periods. To address the shortcomings of available GPS/IMU fusion algorithms mentioned above, this work [66] introduces a cascaded KF to integrate GPS/IMU for trajectory determination in sports applications. This cascaded KF consists of a separate orientation filter cascaded with a position/velocity filter. Accurate orientation obtained from the orientation KF are fed to a gyroscope error KF and navigation KF to estimate navigation parameters in addition to inertial bias errors. Using cascaded linear Kalman filters result in more computational efficiency by reducing matrix operations. Additionally, the use of a separate orientation filter results in more robust orientation estimation during GPS attenuation/blockage. periods.. Experimental. results. for. a. number. of. downhill. snowboarding runs show that the proposed algorithm is 70% and 80% faster than the two common nonlinear KF-based sensor fusion methods, i.e. the EKF and UKF, respectively without sacrificing the tracking accuracy. Additionally the results show that the proposed method can successfully bridge the frequent GPS outage periods of 5 s in skiing and snowboarding with the horizontal position accuracy of less than 2 m. For additional information, the reader is referred to Appendix C or [66].. 3.2.3.. UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking This work [78] extends the application of the method presented in Sec. 3.2.2 to. localization in indoor settings. Among the RF-based systems (e.g., RFID, WiFi and Bluetooth) for indoor localization, UWB technology has been used in this work mainly due to its high precision, reasonable cost, large coverage range (about 400 𝑚2 ) and robustness against multipath effect [79]-[80]. However, materials like metal and liquid can cause interferences in the UWB radio signals [81]. Additionally, in UWB positioning systems, the UWB-derived vertical position tends to be less accurate than the horizontal 16.

(26) position [52]. One of the major reasons behind this is the geometry of UWB sensors and the fact that they are installed almost on the same horizontal plane [82]. With the limited number of UWB sensors, typically, this geometry helps to cover more horizontal area but suffers from the vertical accuracy. The available UWB/IMU fusion algorithms have only considered the tracking problem in horizontal plane [51],[53]-[83]. Nevertheless, there is a need to accurately track trajectory in vertical direction in activities such as jumping and hopping as well as more complex acrobatic motions. Additionally, the previous works on UWB/IMU fusion have only focused on the tracking accuracies under less dynamic human activities such as walking [52]-[53]. However, one potential benefit that IMU can bring is to improve the tracking accuracy under highly dynamic motions where noisy and low update rate measurements of UWB fail to track dynamics of the motions. For the purpose of full motion trajectory tracking and MoCap, which include the 3-D tracking of the root joint (i.e. waist) position/velocity trajectory and lower body kinematics under dynamic conditions, the performance of a wearable IMU integrated with an UWB localization system is investigated in this work. A loosely coupled cascaded two-step KF is used to fuse IMU measurements for orientation estimation. The orientation KF is then cascaded with a position/velocity KF for UWB-aided 3-D inertial tracking. The experimental results, which benchmark the system against a reference camera-based motion tracking system, show that the fusion algorithm can significantly improve the 3-D localization accuracy of the UWB system. In fact, under fast dynamic motion, when the UWB system tends to be less accurate due to its low sampling rate, the fused system can significantly improve the localization accuracy of the UWB system by at least 65%. The fusion algorithm shows the accuracies of about 4.2, 3.6 and 4.9 cm in x, y and z directions, respectively. Furthermore, the algorithm can also accurately track joint angles during fast dynamic activities, with the accuracy of about 2.1 degrees in the case of the knee joint. For additional information, the reader is referred to Appendix D or [78].. 17.

(27) 3.3. Magnetometer-free Motion Tracking 3.3.1.. A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion As mentioned in Sec. 3.2.3, a fused UWB/IMU localization system can provide. accurate estimates can be used for accurate 3-D human position estimation. In the existing loosely coupled UWB/IMU fusion algorithms, magnetic data from magnetometer are used to help the estimation of yaw angle and thus the horizontal position and velocity [51],[53]. However, in indoor environments, the Earth’s magnetic field can be easily perturbed by the presence of ferromagnetic objects [4], [84]-[85], necessitating the need for magnetometer-free localization. Although a nonlinear tightly coupled KF-based fusion algorithm for magnetometer-free UWB/IMU integration has previously been proposed in [52], the complexity and computational costs of the nonlinear approach may not be desirable for low cost, battery powered ambulatory analysis in human localization [52]. This work [86] introduces a novel two-step cascaded KF for a loosely coupled magnetometer-free UWB/IMU integration. It investigates the effect of not using magnetometer for indoor human localization and yaw angle estimation. The proposed algorithm uses two linear Kalman filters. At the first step, i.e. the tilt KF which is based on the work explained in Sec. 3.1.1, tilt angles are estimated. At the second step, i.e. the localization KF, the UWB localization system is fused with the IMU’s accelerometer and gyroscope to estimate position, velocity and the yaw angle. Compared to the magnetometer-aided tracking, using the proposed magnetometer-free algorithm, any indoor magnetic disturbances will no longer affect the horizontal position and velocity estimation. However, the experimental results show that about 20 s of motion is required for the yaw angle to converge which is the trade-off for not using the magnetometer. The experimental results show that the proposed method can accurately estimate yaw angle, and the position and velocity solutions match the ones from an optical MoCap system. For additional information, the reader is referred to Appendix E or [86].. 18.

(28) 3.3.2.. A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture This work [87] extends the magnetometer-free location estimation algorithm in. Sec. 3.3.1 to include MoCap of lower-body segments as well. In addition to localization, the estimation of 3-D orientation for each body segment is required for 3-D posture tracking in a typical MoCap application. However, the presence of electromagnetic disturbances in indoor environment, highly affects the 3-D orientation estimation for body segment. Although a constant magnetic disturbance can be effectively identified and removed by proper magnetometer calibration [88], indoor magnetic disturbances can be from varying sources and change over time. To deal with these magnetic disturbances, model-based sensor fusion [28], threshold-based switching [89] and vector selector [25] approaches were proposed in the literature. Although these approaches are effective for short periods of time (5-10 𝑠), they are drift-prone under varying disturbances and during longer periods of time [90]. This work introduces a magnetometer-free algorithm for lower-body MoCap including 3-D localization and posture tracking by fusing inertial sensors with an UWB localization system and a biomechanical model of the human lower-body. Using this novel KF-based fusion algorithm, the UWB localization data aided with the biomechanical model can eliminate the drift in inertial yaw angle estimation of the lowerbody segments. This magnetometer-free yaw angle estimation makes the algorithm insensitive to the magnetic disturbances. The algorithm is benchmarked against the optical MoCap system for various indoor activities including walking, jogging, jumping and stairs ascending/descending. The results show that the proposed algorithm outperforms the available magnetometer-aided algorithms in yaw angle tracking under magnetic disturbances. In a uniform magnetic field, the algorithm shows similar accuracies in localization and joint angle tracking compared to the magnetometer-aided methods. The localization accuracy of the proposed method is better than 4.5 cm in a 3-D space and its accuracy for knee angle tracking is about 3.5 and 4.5 degree in low and high dynamic motions, respectively. For additional information, the reader is referred to Appendix F or [87].. 19.

(29) 3.4. Regression Model-based Walking Speed Estimation 3.4.1.. Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU Using a single body-worn IMU, regression models have been used as a popular. methodology for walking speed estimation [20], [71], [91]. Accuracy of the regression algorithms depends on the set of variables (e.g. raw acceleration, gravity compensated acceleration, etc.) and the extracted features (time-domain and frequency-domain features). For each IMU mounting location, the choice of variables and features will affect the ultimate walking speed estimation accuracy based on a regression model. Hypothetically, due to the periodic leg swing during walking, a leg-mounted IMU would be ideal in capturing the periodicity of walking. Thus, frequency-domain features calculated for the inertial variables from a leg-mounted IMU would be better representatives of walking speed compared to the ones from a waist-mounted IMU. This work [92] provides a comparative experimental study to shed light on the dependency of walking speed estimation accuracy on various variables, features, and regression methods for two IMU mounting locations including waist and ankle. For the variables, external acceleration is compared to raw acceleration from a tri-axial accelerometer. For the features, the effect of using time-domain and frequency-domain features on the ankle and waist inertial data is investigated. For the regression models, the walking speed estimation accuracy of a GPR model is compared to a Lasso regression model. Among the three variables, external acceleration shows the best results. Based on subject trials, a GPR model shows superior performance compared to a Lasso regression model. In terms of features, when using only frequency-domain features, the estimation accuracy for the waist will suffer significantly compared to the case of using the combined time-domain and frequency-domain features. By using both time-domain and frequency-domain features, waist and ankle-mounted sensors result in similar accuracies: 4.4 cm/s for the waist and 4.7 cm/s for the ankle. When using only frequency-domain features, estimation accuracy based on a waist-mounted sensor suffers more compared to the one from ankle. For additional information, the reader is referred to Appendix G or [92]. 20.

(30) 3.4.2.. Regression model-based walking speed estimation using wrist-worn inertial sensor For longitudinal health status monitoring, among available state-of-the-art inertial. sensing-based wearables, wrist-worn devices are the most user-friendly and compliant that do not limit the freedom of movement and do not require specific dressing style (e.g., wearing a belt in the case of waist-worn sensor). Thus, wrist-worn devices have relatively higher potential to be easily incorporated into daily lifestyle and worn for longer hours. Similar to hip rotation in each gait cycle [20], arm swing motion during walking is a periodic motion pattern that is highly correlated to walking speed: the faster the walking speed, the faster the arm swing motion. However, in walking speed estimation based on regression models, free arm motion necessitates the use of more complex algorithms to manipulate the acceleration and rate of turn information and get a variable that is more representative of the arm swing motion. Although extracting this variable is of high importance (since the accuracy of the regression models depends on the set of chosen variables and the extracted features), it has not been addressed in the existing literature. By using the arm swing motion in walking, this work [93] proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. The arm swing motion is represented by a novel variable called pca-acc, which is highly correlated to walking speed in terms of both temporal and frequency characteristics. To obtain the pca-acc variable, first the tilt angles of the wrist are calculated by applying a KF-based sensor fusion method. Second, principal component analysis (PCA) is employed to find the horizontal acceleration in the direction of arm swing. Experimental results from 15 young subjects showed that using the proposed pca-acc variable will result in significantly better walking speed estimation accuracy compared to the use of raw acceleration variables (𝑝 <0.01). Using a combined time domain (TD) and frequency domain (FD) features of the pca-acc variable, a generalized GPR model resulted in mean absolute error and standard deviation of about 5.9% and 4.7% respectively. Based on the experimental results, the generalized and subject-specific GPR models tend to perform similar except for slow walking regime (speed < 100 cm/s) where a subjectspecific model provided better estimation accuracy. Compared to the generalized least square regression model based on Lasso (LSR-Lasso), the generalized GPR model performed significantly better (𝑝 <0.01) for wrist-based walking speed estimation. 21.

(31) Experimental results from a 10-minute outdoor walking trial demonstrated the feasibility of using the proposed method for wrist-based walking speed estimation in real-world environment. For additional information, the reader is referred to Appendix H or [93].. 22.

(32) Chapter 4. Conclusions and Future Work 4.1. Conclusions and Contributions This thesis was devoted to the development of algorithms for unobtrusive measurement of human body motion and localization using wearable inertial sensors fused with indoor and outdoor localization technologies. The developed algorithms were evaluated through human subjects trials in various environmental settings. The key contributions of this thesis can be summarized as follows: (i). Development of a sensor fusion algorithm to capture 3-D orientation of human body segments using wearable IMU. This algorithm is based on KF with a cascaded structure resulting in more computational efficiency. At the first step, acceleration and angular velocity data are fused to provide estimation of the tilt angles. At the second step, magnetic field information is fused with the angular velocity and the previously estimated tilt angles to estimate yaw angle. Experimental results revealed that the developed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.. (ii). Development of a sensor fusion algorithm to capture vertical position and velocity trajectories. This sensor fusion algorithm makes use of the above mentioned orientation estimation method to estimate tilt angles in order to find the gravitycompensated acceleration in the vertical direction. This acceleration data is then fused with barometric pressure information to estimate the vertical position and velocity trajectories. This method can be used to compensate for the poor accuracy of GPS is vertical direction when used for tracking athletes in outdoor sports such as skiing. The experimental results showed that the vertical trajectory tracking error is less than about 28 cm.. (iii). Development of a GPS/IMU integration algorithm for tracking horizontal position and velocity in outdoor sports. The proposed algorithm uses the above mentioned orientation filter, cascaded with a position/velocity filter. By using cascaded linear Kalman filtering, this method avoids the need to propagate additional states, resulting in the covariance propagation to become more computationally efficient for ambulatory human motion tracking. Additionally, the use of this separate 23.

(33) orientation filter helps to retain the orientation accuracy during GPS outages. Results of the field experiments revealed that the proposed algorithm is computationally much faster compared to the available non-linear approaches and demonstrates improved trajectory tracking during GPS outages. (iv). Development of a UWB/IMU fusion algorithm for simultaneous 3-D trajectory tracking and lower body MoCap under various dynamic activities such as walking and jumping. This method uses wearable inertial sensors fused with UWB localization system using a cascaded KF-based fusion algorithm, which consists of a separate orientation filter cascaded with a position/velocity filter. Additionally, to further improve the joint angle tracking accuracy, anatomical constraints are applied to the knee joint. The results showed that the proposed system can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking. The obtained accuracy of the system for 3-D body localization and knee joint angle tracking for fast motions were less than 5 cm and 2.1 degrees, respectively.. (v). Development of a magnetometer free IMU/UWB fusion method for 3-D indoor human localization and lower body MoCap. Using this KF based fusion algorithm, the UWB localization data aided by the biomechanical model can eliminate the drift in inertial yaw angle estimation of the lower-body segments. This magnetometerfree yaw angle estimation makes the algorithm insensitive to the magnetic disturbances. The experimental results showed that this algorithm outperforms the available magnetometer-aided algorithms in yaw angle tracking under magnetic disturbances. In a uniform magnetic field, the algorithm showed similar accuracies in localization and joint angle tracking compared to the magnetometer-aided methods. The localization accuracy of the proposed method is better than 4.5 cm in a 3-D space and its accuracy for knee angle tracking is about 3.5 and 4.5 degrees in low and high dynamic motions, respectively.. (vi). Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU. The comparison was based on different sets of variables, features, mounting locations and regression methods. An experimental evaluation was performed on healthy subjects during free walking trials. The results showed better accuracy of GPR compared to Lasso regression. Among the variables, external acceleration tended to provide improved accuracy. By using both time-domain and frequency-domain features, waist and ankle-mounted sensors resulted in similar accuracies: 4.4 cm/s for the waist and 4.7 cm/s for the ankle. When using only frequency-domain features, estimation accuracy based on a waist-mounted sensor suffered more compared to the one from ankle.. (vii) Development of a regression model-based walking speed estimation using a wristworn IMU. Time-domain and frequency-domain features of arm swing motion in walking, was used in a regression model for longitudinal walking speed estimation using a wrist-worn IMU. To make the best use of arm swing’s inertial information, a novel kinematic variable was developed that finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable was obtained by applying sensor fusion on tri-axial accelerometer and gyroscope data to find the tilt 24.

(34) angles followed by the use of PCA. The experimental results showed that using the proposed variable can significantly improve the walking speed estimation accuracy when compared against the use of raw acceleration information. The resulting walking speed estimation mean absolute error and standard deviation were about 5.9% and 4.7%, respectively.. 4.2. Future Work In order to truly realize an ambulatory human motion tracking for sports and health monitoring applications, the work presented in this thesis needs to be progressed further. The future work can be mainly divided into the following three themes. One of the contributions of this thesis was the development of vertical trajectory tracking algorithm by integration of IMU and MEMS barometric pressure sensor. For evaluation of this algorithm only indoor experimental results were considered which may have some limitations if directly applied to outdoor sports such as skiing and snowboarding. While the lab-based subject trials were suited for the purpose of this thesis, which was the theoretical and proof-of-concept demonstration of the proposed algorithm, the ultimate goal is to expand it for vertical jump trajectory tracking in skiing and snowboarding. In future, field testing should be carried out, in order to generalize the proof-of-concept results to outdoor skiing/snowboarding activities, especially under temperature variations and during high wind or speed conditions when the altitude measurements from the barometric pressure sensor might be disturbed and become unreliable. A magnetometer-free lower body localization and MoCap system was introduced in this thesis in order to remove the errors caused by indoor magnetic disturbances in indoor environment. Although the ferromagnetic disturbances are mainly caused by the underground electrical equipment that are closer to lower-body (e.g., electrical cables), the ferromagnetic objects around the room may also affect the MoCap accuracy of the upper-body as well. Thus the magnetometer-free lower body MoCap algorithm developed in this thesis should be extended to upper body. With respect to the wrist-based walking speed estimation, the experimental results presented in this thesis may have some limitations. The proposed method is 25.

(35) based on the assumption of free arm swing during walking. In reality, this assumption would not be satisfied in occasions such as carrying a bag, putting hand in the pocket, walking with walkers, etc. However, while there is no arm swing in these situations, since the arm is now fixed to the trunk, the acceleration profile of the wrist will be similar to that of the trunk. Thus, in future, these situations should be identified using a proper activity classification algorithm and a separate regression model should be trained for walking speed estimation in such cases. Additionally, the true value of walking speed estimation for health monitoring is for older adults when the decline in their walking speed can be considered as an early marker of their neurodegenerative disease. As future work, clinical investigations should be carried out by collecting real-world data from elderly subjects and fine-tune the regression model for longitudinal walking speed estimation in older adults.. 26.

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