MUSCLE ACTIVATION AND DRIVING PERFORMANCE DIFFERENCES BETWEEN STATIC AND DYNAMIC DRIVING SIMULATION CONDITIONS

MUSCLE ACTIVATION AND DRIVING PERFORMANCE

DIFFERENCES BETWEEN STATIC AND DYNAMIC DRIVING

SIMULATION CONDITIONS

The purpose of this work was to compare simulator and surface electromyography (sEMG) variables between fixed base (FBDS) and dynamic driving (DDS) simulation conditions using a hexapod robot based driving simulator during 90 degree turning manoeuvers. Nine males and nine females, who wore a light-weight head mounted display to provide the visual input, drove faster in the DDS leading to faster turn exit velocities and turn completion times. For sEMG variables, one of the most common findings was that when making left turns, higher muscle activity was observed in the DDS. This suggests that DDS driving does differ from FBDS driving. Information provided by this work shows that simulator movement affects muscle activation, therefore, care should be taken when interpreting results from FBDS to not assume that similar results would be found in DDS, and by extension naturalistic driving.

INTRODUCTION

Driving simulators are often used to assess driver responses to hazards (Matthews et al., 1998; Underwood, Crundell & Chapman, 2011) as well as proposed road designs (Bella, 2008). Many parameters can be investigated thereby allowing researchers to perform experiments without endangering participants. Simulators have also been used for training (Roenker, Cissell, Wadley & Edwards, 2003) as well as investigations on general driving performance (Matthews et al., 1998), driver physiological responses (Rizzo, 2004), and assessments of new technologies such as infotainment systems (Slob, 2008) and collision warning systems (Lee, McGehee, Brown & Reyes, 2002).

One of the ways driving simulators can be characterized is by whether they move or not. Fixed base driving simulators (FBDS) are static in nature and, therefore, do not move; dynamic driving simulators (DDS) are commonly mounted on a platform that provides 6 degrees of freedom (x, y, z, roll, pitch, yaw) in a 360-degree field of view (FOV) fully immersed environment (Slob, 2008). Due to the more complex structural components and control algorithms required to run DDS, DDS tend to be more expensive than FBDS, therefore, FBDS are more common.

While some researchers have focused on driving simulator variables such as steering wheel angle, lane position and perception response time to potential hazards (Lee et al., 2004), others have focused on quantifying physiological responses such as muscle activation levels (YaHui, XueWu, Ryouhei, Takahiro & LiMing, 2012). Results from studies which have quantified muscle activity have been used to help improve car designs that aim to maximize comfort levels. Previous studies examining muscle activity using surface electromyography (sEMG) have been conducted in simulators (Balasubramanian and Adalarasu, 2007; Katsis, Ntouvas, Bafas & Fptoados. 2004) as well as during naturalistic driving (Katsis et al., 2004). Simulators that have been used to quantify muscle activity while driving have also examined driver muscular control strategies while in different vehicles.

Although previous studies have used either a FBDS or a DDS, to the authors’ knowledge, a direct comparison has not been made between the two driving simulation conditions in identical simulators. Therefore, the extent to which the presence or absence of motion affect muscle response is not known.

This study aims to reduce the knowledge gap by comparing and contrasting muscle physiological response in the form of surface electromyography (sEMG), as well as driving behavior by comparing vehicle velocity, and steering wheel angle during 90 degree turns in a FBDS compared to a DDS. Parameters were quantified through 90-degree turns, as it is believed that any differences between the two simulation conditions would be amplified during turning manoeuvers. This is of interest because accidents often occur at intersections when vehicles are turning, therefore, it would be interesting to know if different results are obtained in FBDS versus DDS. If there are differences, this could influence study result generalizability between the two simulator motion modes.

METHODS Experiment Set-Up

A frame consisting of an adjustable seat, steering wheel, and driving pedals were placed on top of a Mikrolar R3000 hexapod robot (Mikrolar Inc., Hampton, NH, USA) (Figure 1). The hexapod is a high precision positioning system that can provide six degrees of freedom of movement through six fixed length actuators, which are coupled with six independent motorized trucks. When moving, the device serves as the DDS, and when not moving as the FBDS. SCANeRStudio™ (Oktal, Meudon, France) version 1.5 was used to communicate with the MRLHPOD Version 2.5.0 robot software (Mikrolar Inc., Hampton, U.S.A) to move the robot.

The SCANeRStudio™ API was used to develop a custom application that allowed for dynamic movement using

the physics engine dynamic models. Visual studio C++ was used to create a module to interface between the hexapod control application as well as the SCANeR Studio™ simulation engine. As a result of using two computers, a TCP/IP socket layer was used as a means of communication between software.

To provide the participant with a view of the road similar to one that would be observed when driving, all participants wore an Oculus RiftTM _{DK2 (Oculus VR, CA, United States)}

virtual reality head set. The Oculus RiftTM _{DK2 has two}

display screens, each having a resolution of 960x1090 pixels, with a refresh rate of 60Hz. The HMD was integrated with SCANeRStudio™ to display the driving environment to the participants. The display screens of the HMD were set relative to the direction the participant was facing using the HMD built-in accelerometer sensors.

In addition to containing the physics engine, the software also recorded driving parameters such as time, velocity, yaw position and steering wheel angle, all of which were sampled at 60 Hz.

Two custom maps, which included urban and rural routes were designed through SCANeRStudio™. The first map was used for participant familiarization and took approximately 5 minutes to complete. Participants drove through the familiarization road twice, starting with a static drive followed by a dynamic drive. The reason the static drive was completed first was because participants were more likely to develop simulation adaptation syndrome (SAS) during the static drive thus quickly precluding study participation. Towards the end of each of the familiarization drives, participants would perform a left turn in the first 5 minute familiarization drive and a right turn in the second 5 minute familiarization drive to get accustomed with the simulation environment and turning manoeuvers. For the experiment drives, the order in which they completed the DDS and the FBDS drives were counterbalanced. There were a total of four turns in each of the DDS and FBDS drives which included two left and two right turns. The participant had to make a 90-degree angle yaw change for the turn to be deemed acceptable. Each route took approximately 10 minutes to complete. Participants were instructed to drive as they normally would and there was minimal interaction between the experimenter and the participant to ensure that subjects focused on driving.

Participants

Following approval by the University of Guelph Research Ethics Board, 9 male (22.78±2.05 yrs; 82.3±13.72 kg; 1.81±0.13 m) and 9 female (21.44±2.46 yrs; 75.44±24.60 kg; 1.63±0.09 m) participants provided informed consent prior to study participation. All participants were licensed to drive and were pre-screened for SAS using a validated pre-screening SAS questionnaire (Kennedy, Lane, Berbaum & Lilienthal, 1993). Those for whom the questionnaire revealed a high risk for SAS were excluded from study participation. Data from participants who developed SAS over the course of study participation were removed.

Data Segmentation

During the experiment, participants were stopped at a stop sign controlled intersection prior to performing the right or left hand turn. The beginning of the turn was identified as the point at which the vehicle began to move after coming to a full stop, and the end of the turn was determined as the point at which the vehicle was parallel to the median strip lines. Ninety-degree turns were first manually identified from the driving simulation video by observing the vehicle. The selected turns were time stamped and the yaw angle corresponding to the time stamp was processed through custom MATLABTM _{code (R2015a, Mathworks, Natick, MA)}

to verify that the vehicle did in fact complete the required 90-degree turn.

The following variables were then extracted from the data: turn completion time(s), velocity, yaw angle, and steering wheel angle. From these data, the following dependent variables were quantified: maximum velocity, angle at maximum velocity, minimum velocity, angle at minimum

Figure 1 Mikrolar R3000 hexapod robot driving simulator velocity and maximum steering wheel angle. Custom MATLABTM _{code was used to further break the turn into}

angles based on the yaw angle. This code identified the points at which the 90-degree turn was completed and then segmented the data streams accordingly.

Surface Electromyography

shaved and lightly abraded with medical grade sandpaper and the electrodes were individually cleaned with disposable alcohol swabs prior to placement. Electrodes were placed parallel to the muscle fibres of the muscle of interest according to Criswell (2010). Following electrode placement, resistance was measured with a standard ohmmeter and if impedance exceeded 20kΩ (i.e., less than 100 times the impedance of the amplifier), electrodes were removed and replaced until the condition was met. Two disposable reference electrodes (Dermatrode ®, Delsys Inc., Boston, MA) were used; one for each system, and they were placed on the lateral epicondyle of the right and the left arm. Surface EMG signals were recorded using Bagnoli-8 eight channel Delsys and Myomonitor IV™ (Delsys Inc., Natick, MA) systems, with the Bagnoli system recording data through Nexus v2.2.3 (Vicon©Motion System Ltd., Oxfordshire, United Kingdom). The sampling rate was set to 1000Hz, with a bandwidth of 20-450, a common mode rejection ratio of >92dB, and a fixed gain of 1000.

To normalize the EMG data, Reference Voluntary Contractions (RVCs) were performed bilaterally in accordance with Criswell (2010) and Kumar, Narayan & Amell (2003). To perform the RVCs, participants contracted their muscles with their maximum possible force, then held the contraction for a total five seconds per muscle. Since for most of the bilateral muscles, RVCs were performed simultaneously, this initial set of contractions took approximately 2 minutes to complete. After the last muscle was contracted, a rest period of 4 minutes was provided to minimize fatigue effects. RVCs were then performed a second time resulting in the RVC process taking approximately10 minutes to complete.

EMG data were post-processed using custom MALAB™ code by full wave rectifying, band passing (20-400Hz) and then linear enveloping the data using a second order 6Hz dual pass Butterworth filter (Winter, 2009). Once linear enveloped, data were clipped to be the same length (on a time base) as the turning data obtained from SCANeRStudio™ thereby excluding data outside of 90-degree turns. From the clipped EMG data, peak and RMS values were determined for each muscle to provide an indication of the maximal and average activation levels of a given muscle over the course of a turn. Statistical Analyses

Over the course of the two drives, participants performed two 90-degree turns in the FBDS condition as well as in the DDS condition with a right and a left turn completed in each condition. To analyze the results, analysis of variance procedures was conducted. For EMG variables, the independent variables consisted of Driving Condition (FBDS or DDS), Turn Side (Right or Left), Sex (Male or Female) and Muscle Side (Right or Left). The dependent EMG variables were root mean square (RMS) and Peak EMG (EMGPeak) for

each of 12 muscles. For the simulator variables determined from SCANeRStudio™, the independent variables were Driving Condition (FBDS or DDS), Turn Side (Right or Left) and Sex (Male or Female), whereas the dependent variables were initial velocity, end velocity, turn time, turn angle at which maximum velocity occurred, turn angle at which

minimum velocity occurred and maximum steering wheel angle. When conditions of normality were not met, data were transformed and became normal using a Johnson or Box Cox transformation.

The level to declare a significant difference between means was set at p≤0.05. When appropriate, differences between means were assessed using Bonferroni t-test post-hoc procedures. For all analyses, and to account for multiple measures being obtained on the same participant, participant was included in all statistical models as a random effect. All analyses were performed using MinitabTM _{18 (Minitab, State}

College, PA, USA).

RESULTS Driving Simulator Variables

A comparison of driving simulator data while driving between different conditions (FBDS vs DDS) revealed that there were significant differences in driving behavior between conditions. When in the DDS, participants reached a significantly higher (p=0.03) maximum velocity (9.40±3.87 m/s versus 8.02±2.73 m/s) through the turn. Additionally, while in the DDS, participants were also found to exit the 90-degree turn at a significantly higher velocity (p=0.03) (7.11±3.02 m/s versus 5.93±2.39 m/s), resulting in a significantly shorter turn duration (p=0.05) (10.89±4.50 versus 13.53± 6.44 s).

Surface Electromyography Variables

EMGPeak values for the DDS were significantly higher than the

FBDS for the UT (p=0.000) and SCM (p=0.000). RMS values were significantly higher in the DDS than the FBDS for the UT (p=0.000), FCR (p=0.01), ECR (p=0.000), SCM (p=0.000) and PM (p=0.02).

Further comparisons revealed a significantly higher EMGPeak (p=0.02) and RMS (p=0.02) in the UT when making

left turn turns in the DDS when compared to both right and left turns in the FBDS.

Significantly higher EMGPeak values were observed for

the UT (p=0.02) for females performing left turns in the DDS when compared to males performing both right and left turns while in the FBDS and females performing right turns in both the FBDS as well as the DDS. For FCR EMGPeak,, all 4 female

combinations of turn sides and simulator condition were larger than all male turn sides and simulator conditions (p=0.01).

Analysis of the SCM EMGPeak (p=0.000) and RMS

(p=0.000) revealed that the left SCM muscle was significantly lower than the right SCM muscle. In addition, both the EMGPeak (p=0.000) and RMS (p=0.000) of the left LD were

significantly higher than the right LD. The UT EMGPeak on the

right side was significantly larger than the left (p=0.000).

When performing left turns, participants had

significantly higher RMS (p=0.046) of the FCR as well as EMGPeak (p=0.032) and RMS (p=0.001) for the ECR muscle

when compared to right turns. FCR EMGPeak (p=0.03) was

significantly higher for left turns in the DDS than either right or left turns in the FBDS. For the TC, EMGPeak (p=0.003) and

DDS than the FBDS. For the TC, both the EMGPeak (p=0.006)

and the RMS (p=0.05) revealed that left muscles in the DDS displayed significantly higher values than right muscles when making right turns in the DDS, as well as when making left and right turns in the FBDS. Further investigation of the TC RMS (p=0.05) found that female left muscle side when performing left turns was significantly higher when compared to male right and left side muscles when performing left turns. Female left muscle side values when performing left turns were also higher when compared to female right side muscles when making left turns. It should be noted that female right muscle side when making left turns were also significantly higher when compared to male left and right side muscle when making left turns as well as for male right muscle side when performing right turns.

DISCUSSION

Ideally, naturalistic driving would be used for every driving study, however, for safety reasons, simulators are often used as a safer alternative. Though it has been shown that mistakes such as excess in speed, and turning speed, as well as suddenly stopping are committed less often in on road experiments (Mahew et al., 2011), high fidelity simulated driving has been shown to provide data that is similar to naturalistic drives (Stanton, 2012).

It could be argued that due to the motion cuing present in DDSs, a road feel is provided that is not present in FBDSs [10]. It has been shown that people drive faster in straight paths when using FBDSs, but while in a DDS, people claim that the motion cuing has helped them determine how fast they are turning on a curve (Redmond, Kemeny, Droulez & Berthoz, 2001), which can potentially help them make the turn in a more safe and efficient manner.

This study found the maximum velocity and the final velocity were significantly higher in the DDS leading to significantly quicker turns. The lack of motion in the FBDS could have also elicited more cautious driving behavior. Therefore, the driver may have opted to drive at slower speeds due to the increased difficulty of predicting vehicle speed due to a lack of kinesthetic feedback from the simulator.

Vibrations are always present in naturalistic and DDS driving and the vibrations can cause drivers to tense their muscles in order to maintain stability (Zheng et al., 2011). Zheng et al. suggested that lateral acceleration through slalom driving increases muscle activation levels of the SCM as these muscles work harder in order to keep the head stable (Zheng et al., 2011). Similar results were seen in the current study, where SCM RMS values were higher in the DDS. It could be argued that the weight of the HMD might have had an influence in these results, however, the HMD used (470 grams) was quite light and would have affected the FBDS almost as much as the DDS. The differences found in the UT and SCM can potentially be explained by the fact that during dynamic simulations, the increased movement of the hexapod robot may cause the image within the field of view to shift, causing the participant to alter their head posture and upper body to maintain the desired image within the HMD screens

(Forde et al., 2011; Harrison et al., 2007b). Furthermore, significant differences found between TC EMGPeak and SCM

RMS when performing left turns in the DDS compared to right and left turns in the FBDS indicate that there was higher muscle activity through longer turns. Left turns take longer (13.94±7.74 versus 11.18±4.96 s) as one has to cross an entire lane in order to reach the desired lane, potentially forcing drivers to adjust their steering position in the DDS to regain the neutral position and/or stabilize the image allowing the participant to check the vicinity of their vehicle to ensure a safe turn. This probably occurs because left turns require more attention compared to right turns (Larsen and Kines, 2002). This contrast became apparent with the UT and FCR since females were shown to have significantly higher muscle activity when compared to males and therefore could be susceptible to fatigue at a quicker rate when compared to males. Furthermore, when only looking at muscle side of the TC, similar results were shown when sex, turn side and muscle side were considered. DDS left turns tended to produce elevated muscle activity levels when compared to FBDS left turns and in many cases right turns. This may lead to the conclusion that if research is to be done in a DDS in conjunction with an HMD device, researchers need to be aware of this elevated muscle activity in a DDS when compared to a FBDS. This is especially true for long experiments that require multiple days of performance without adequate rest periods to the driver which involve simulators that produce vibrations.

Steering is a critical component of driving, and one of the muscles that becomes active while steering is the FCR (Abbink, Mulder & Van Paasen, 2011). Through this study, it was found that EMGPeak of the FCR was significantly higher

while in the DDS. This is possibly due to the movement and vibrations found with the DDS, which force participants to over-steer and/or under-steer in order to make the appropriate corrections to successfully complete the turn. This can lead to an increased level of FCR muscle activity as it is the muscle associated with gripping the steering wheel (Abbink et al., 2001).

The BB, and MD are the primary power suppliers when performing handling steering manoeuvers (YaHui et al., 2012). The highest forces occur during the upward movement of the arm which theoretically occurs when the turn begins. When steering clockwise, the right TC becomes active, as it is the agonist muscle, whereas the MD, BB, as well as part of the PM are responsible for stabilization. However, when counter steering, the AD, MD, TC, and PM were the primary movers while the parts of the PM, TC are considered stabilizers (YaHui et al., 2012). For this reason, the FCR and ECR should be active and in fact are seen to be active when the interaction between left and right turns are observed. Studies have found metabolic response differences between muscle sides when heavy (> 1.6Kg) HMDs are used (Harrison et al., 2007a), however, the results of the current study show that even when a relatively light HMD is used, the response differences persist.

muscle side, it was found that there were differences involving the LD, SCM and TC. For the LD, the left side muscles had higher activity when compared to the right side. For the SCM and UT, the right side had higher muscle activity than the left. When looking at the simulated condition and turn side interaction, it was found that the UT EMGPeak during left turns

in the DDS was significantly higher than making either left or right turns in the FBDS. Similarly, the SCM displayed significantly higher EMGPeak and RMS for left turns in

dynamic conditions, when compared to left turns in static conditions. DDS movement may have caused higher musculoskeletal activity as participants adjusted their posture to regain the desired image within their FOV as previously discussed. However, when simply looking at left turns, it was seen that the ECR EMGPeak and RMS as well as the FCR RMS

displayed higher levels of muscle activity when making left turns relative to right turns.

The purpose of this study was to compare differences in simulator and sEMG variables between fixed base and dynamic driving simulation conditions. In doing so, a more complete understanding of muscle activation and driver behavior was determined. This study investigated driving parameters that have been previously used to describe driving behavior. It was found that during DDS driving, subjects would drive at a faster velocity leading to higher exit velocities and therefore quicker turn completion times. This led to the assumption that while in a FBDS, participants would exhibit increased levels of cautious behavior possibly due to the lack of simulator motion during the turn.

Several muscles associated with driving were assessed to determine differences between driving in a FBDS compared to a DDS while subjects were outfitted with a commercially available light-weight HMD as the display modality. One of the most common findings was that when making left turns, higher muscle activity was observed especially in the DDS. This suggests that DDS driving does significantly differ from FBDS driving. Therefore, those who perform research in DDS with the use of a HMD should be aware of such differences and as such, the number of exposure times as well as the length of the experiment should be considered in their experimental design to minimize fatigue effects. Information provided by this work shows that simulator movement does affect muscle activation, therefore, care should be taken when interpreting results from FBDS to not assume that similar results would be found in DDS, and by extension to naturalistic driving.

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