11 Bicycles are more difficult to control at low speeds due to the vehicle’s unstable low-speed dynamics. This issue might be exacerbated by factors such as aging, disturbances, and multi-tasking. To address this issue, we developed a second prototype ’balance assist system’ with Royal Dutch Gazelle and Bosch eBike Systems at Delft University of Technology, which includes an electric motor capable of providing additional steering torque to that of the rider. We conducted a study with 18 older and 14 younger cyclists to first examine the effect of aging, disturbances, and multi-tasking on cycling at lower forward speeds, and evaluate the effectiveness of the system in improving the self-stability of the rider-bicycle system while facing these challenges.12 13 14 15 16 17 18 19 The study consisted of two scenarios: a single-task scenario where participants rode the bicycle on a marked narrow straight-line track, and a multi-task scenario where participants performed a shoulder check task and followed visual cues while tracking the straight-line. We introduced handlebar disturbances using the steer motor also in half of the trials in both scenarios. All trials were repeated with and without the balance assist system. We calculated the bicycle mean magnitude of roll and steering rate – as indicators of bicycle balance control and steering effort, respectively – and the rider’s mean magnitude of lean rate with respect to the ground to investigate the effect of the balance assist system on rider’s lateral motion.20 21 22 23 24 25 26 27 Our results showed that aging, disturbances, and multi-tasking increased the roll rate, but the balance assist system was able to significantly reduce it. The effect of the balance assist system in reducing the roll rate in all conditions was more significant in older cyclists. Disturbances and multi-tasking increased the steering rate, which was successfully reduced by the balance assist system. Aging did not significantly affect the steering rate. The rider’s lean rate was not significantly affected by age, disturbances, or the balance assist, indicating that the upper body plays a minor role when riders have good steering control authority.28 29 30 31 32 33 34 Overall, our findings suggest that lateral motion and steering effort can be affected by age, multi-tasking (distractions), and handlebar disturbances which can endanger cyclists’ safety, and the balance assist system has the potential to improve cycling safety and reduce the incidence of single-actor crashes. Further investigation on riders’ contribution to control actions is required.35 36 37 38 INTRODUCTION 39 Cycling is an eco-friendly means of transport that enhances the healthy lifestyle and is favored by many 40 people. Over the past decade, there was an increasing societal interest in electric bicycles (e-bikes) where 41 the number of e-bikes sold in Europe increased from 0.5 million in 2009 to 3 million in 2019 (Statista). 42 E-bikes enable riders to cycle for longer duration and distance by reducing physical fatigue (Hoj et al., 43 2018). However, with increased numbers of e-bikes, bicycle accidents due to inadequate steering and 44 balance control by older cyclists have increased (Lefarth2021; Berk2022). 45 Bicycles are statically unstable but under certain conditions, e.g., higher forward speeds and rider’s 46 control, can become stable (Astrom et al., 2005). In The Netherlands in 2020 70% of cycling crashes 47 were reported to be single-actor with slippery surfaces and loss of balance as the main causes (Krul et al., 48 2022). Bicycle balance control requires a mixture of passive (bicycle’s self-stability) and active (rider) 49 control to ride the bicycle in a stable balance state such that the rider-bicycle system could maintain or 50 quickly restore if subjected to disturbances. Bicycle dynamics may be affected by a lot of factors, such 51 tyre characteristics (Dell’Orto et al., 2022), road unevenness, and wind disturbance (Schwab et al., 2018). 52 Therefore, keeping the bicycle balanced (especially at low forward speed), pedaling, and steering requires 53 continuous physical and cognitive effort. In this context, an additional external disturbance makes it more 54 challenging for the rider to be balanced ((SWOV, 2017); (Schwab et al., 2018); (Afschrift et al., 2022)). 55 Balance control in older adults due to the degradation in sensory and motor organs is poorer than 56 young adults ((Alizadehsaravi et al., 2020); (Afschrift et al., 2022)). Older cyclists have higher roll 57 rate and steering motion compared to middle-aged cyclists in low speed and multi-task cycling, which 58 indicates age-induced difficulty to control the bicycle’s inherent unstable motion (Kov ´acsov ´a et al., 2016). 59 In addition to aging, variety in riding skills also leads to different bicycle postural control strategies (Cain 60 et al., 2016) which might in turn also vary in response to internal and external disturbances. To improve 61 safety, older adults, unskilled cyclists, and regular cyclists in challenging situations could benefit from an 62 enhanced balance control. 63 After the first prototype presented in (Nieuwenhuizen and Schwab, 2017), we developed the second 64 prototype balance assist bicycle together with Royal Dutch Gazelle and Bosch eBike Systems aiming 65 to increase safety by enhancing the bicycle’s ability to not easily become unstable (i.e., robust balance 66 control). We hypothesized that external disturbances and multi-tasking affect the bicycle motion and 67 that our balance assist system improves the stable damping response in steer and roll rate. We also 68 hypothesized that older cyclists have less lateral control authority than younger cyclists and balance 69 assist system is more effective for older participants. Reduction in roll rate (lateral motion) and steering 70 rate variables are expected based on the reduced demand for compensatory and acute steering control, 71 respectively, as balance assist system applies enough control input to maintain or regain the bicycle’s 72 balance. 73 METHODS 74 The aim of the study was to evaluate the effects of the balance assist system in situations that cycling is 75 challenging especially for older cyclists. 76 Design and implementation of the controller 77 We first simulated the uncontrolled benchmark bicycle motion with a rigid rider (Meijaard et al., 2007) 78 and replicated the results that in forward speeds between 1 to 10 (m/s) the bicycle is laterally unstable, 79 except for a range between about 4 to 6 (m/s) (this range depends on the bicycles and rider’s dimensions 80 and physical properties) that the bicycle is laterally self-stable (Meijaard et al., 2007). We simulated 81 the benchmark bicycle motion with a rigid rider and then added the speed dependent feedback control. 82 Our control aim was to stabilize the system at all forward speeds and the capacity to reject disturbance 83 using a controller with the concept of the “steer-into-the-fall” (Schwab et al., 2008). In an uncontrolled 84 bicycle the weave mode is unstable at low forward speed (positive real parts of the eigenvalues shown 85 with rigid cyan line in Figure 1) and the capsize is unstable at higher forward speed (positive real parts of 86 the eigenvalues shown with rigid dark blue line in Figure 1). We take the bicycle roll and steering angles 87 and rates to be the states of the system with a ’zero-roll rate’ setpoint, and steering torque is an output 88 of the controller (and thus input to the bicycle-rider system). We applied control gains on roll rate and 89 steering angle feedback at low forward speeds ( <4.7 (m/s)). We manually tuned the gains to maximize 90 stability across much of the speed range while also minimizing required steering torque. We showed that 91 with this controller the region of stability in the bicycle shifted to lower forward speeds (1.3 (m/s)) and at 92 higher speeds bicycle motion became marginally stable compared to an uncontrolled motion (Figure 1). 93 Since simulation model is a simplification that does not consider the rider’s relative motion to the bicycle, 94 slightly different results are to be expected in real scenarios. 95 Instruments 96 The participants rode a Gazelle step-through city electric bicycle (Arroyo C8 electric), that has been 97 modified as a balance assist bicycle with a custom direct drive steering motor embedded in the headtube 98 2/12 Figure 1. Eigenvalues from the linearised stability analysis for the bicycle, where the solid lines correspond to the real parts of the eigenvalues and the dashed line corresponds to the imaginary part of the eigenvalues, in the forward speed range of 1 to 10 (m/s) without (left panel) and with (right panel) the controller. (Figure 2). This motor is meant to provide an additional torque between the headtube and the steer tube, 99 helping the rider in the steering maneuver. We instructed the participants to cycle with a self-selected 100 ‘constant’ speed (Appendix 1) in the range of 2 to 5 (m/s) on eco mode (lowest propulsion assist level), to 101 take advantage of the bicycle’s instability at low speeds. 102 With balance assist prototype 2, the bicycle’s forward speed, roll and steer angle and rate are known. 103 Balance assist bicycle is equipped with a wheel speed sensor on rear wheel, Bosch steering angle sensor 104 at the steering tube with 0.1-degree absolute physical resolution and 100 (Hz) sampling rate, and data 105 acquisition plus control boards in its rear luggage rack. The steer motor applies up to 7 (Nm) torque 106 between the rear frame of the bicycle and the steering tube. We tuned the control gains of a simulated 107 control algorithm to stabilize the balance assist bicycle at much lower speeds by generating a corrective 108 steering torque. The steer motor directly applies torque to the steer to stabilize the bicycle’s roll rate at 109 0 (deg/s) setpoint. 110 We evaluated the effectiveness of the balance assist system in young and old cyclists by simulating 111 a series of conditions comparable with real-life cycling challenges. To directly investigate the rider’s 112 motion, a 3-axis IMU sensor (Shimmer research Ltd, Ireland) was mounted on the spine at the T7 level by 113 an elastic strap on the back of the participant (Appendix 1, Figure S1) prior to performing the experiment. 114 Participants 115 32 participants (18 old, 67 ±4 years old, 174 ±8 (cm), 83 ±14 (kg), 6 females; and 14 young, 23 ±2 years 116 old, 179 ±10 (cm), 71 ±12 (kg), 3 females) participated in this study. The participants were recruited by 117 advertisements. All participants signed a written consent form and were able to cycle and had no balance 118 disorders or history of injury or fall caused by instability over the last year. The Human Research Ethics 119 Committee of the Delft University of Technology (The Netherlands) approved the experiments (Letter of 120 Approval 2080). 121 Experimental procedure 122 All participants first cycled for 2-5 minutes to get familiarized with the bicycle with and without the 123 balance assist system (blind setup), and to reduce the habituation effect throughout the experiment. Then 124 participants performed 16 trials divided in two scenarios (single- and multi-task cycling; Appendix 1, 125 Figure S2). 126 Single- and Multi-task Scenarios 127 A common and simple task often experienced during natural cycling is tracking a constant heading without 128 deviating laterally from a straight path, for example when you ride along a straight cycle path alongside 129 a fellow cyclist, along a narrow cycle path, or close to a cycle path edge. To mimic this task, we asked 130 3/12 Figure 2. Second prototype Balance assist bicycle at Delft University of Technology in collaboration with Royal Dutch Gazelle and Bosch eBike Systems. The steer motor and the data acquisition box (DAQ) are annotated in the picture. our participants to ride at a constant self-selected low speed (2 to 5 (m/s)), along a 30 (m) straight line 131 highlighted with a 5 (cm) width road-tape on the ground. 132 A common multi-task scenario was simulated by asking the participants to do a shoulder check task 133 and follow instruction corresponding to the identified visual cues while tracking the above-mentioned 134 line. The instruction was to look back at the starting point over their preferred shoulder the moment they 135 reached the red cone in the middle of the track. At the starting point the researcher was holding up a cone 136 randomly in her left or right hand. Participants were instructed to identify that direction and lift/place 137 back their corresponding hand off/on the handlebar while following the track as closely as possible. 138 Each scenario includes four conditions with one repetition, resulting in a total of eight trials (2 Balance 139 assist states (on/off) x 2 Disturbances states (on/off) x 2 repetition) per scenario. All participants have 140 data available for the single-task scenario. However, due to system malfunction under the high load of 141 self-induced perturbation using the steer motor in hot weather, four participants (3 older and 1 young) had 142 missing data in the multi-task scenario and were excluded from multi-task scenario statistical analysis. 143 For the rider’s upper body lean rate, data from 24 participants were available, including 6 young and 18 144 older participants. 145 Disturbances 146 In half of the trials (in total and per scenario) participants were subjected to small disturbances induced by 147 the steering motor when the balance assist system was (de)activated (Appendix 1, Figure S3). The purpose 148 was to evaluate the bicycle behavior when an unwanted disturbance applies to the bicycle and causes 149 difficulty in control, such as when you hit a bump in the road (short-duration) or front rack cargo or the 150 wind gust pulls the steer in an undesired direction (long-duration). The disturbances were implemented 151 by 3 square wave pulses with random intervals resulting in ∼1.2 (Nm) steering torque. This perturbation 152 is significant because steer torques during straight-line riding tasks are less than 5 Nm (Moore, 2012). 153 The disturbances began one second after the forward speed reached 2 (m/s) for the first time in that trial. 154 The durations of the disturbances were 0.5 (s) and 1 (s) in single-task and multi-task cycling, respectively. 155 Data Analysis 156 We collected time series data from various sensors, including the steering angle sensor, the rear frame 157 IMU, and the IMU placed on the rider’s torso. We analyzed the data to obtain the bicycle’s steering rate, 158 roll rate, and rider’s upper body lean rate as the outcome measures (dependent variables). 159 4/12 To study the effect of different conditions on these dependent variables, we first extracted and 160 segmented relevant data from the sensors, along with forward speed and yaw motion data. We manually 161 identified the start of cycling when the forward speed increased from 0 (m/s). We then divided the time 162 series data into two phases: the transient phase when accelerating from zero to a steady-state phase where 163 the speed was approximately constant until the end of the track. For analysis, we used the steady-state 164 phase of the time series data as a segment of interest. 165 To determine the start indices for each segment of interest, we used the first index at which forward 166 speed reached 1.5 m/s in the accelerating phase. To determine the end indices for each segment of interest, 167 we used two criteria: the first index at which either forward speed reached 1.5 m/s in the decelerating 168 phase or the absolute difference in yaw rate was greater than or equal to 4, indicating a change in cycling 169 direction at the end of the cycling track. We chose 4 deg/s yaw rate as a threshold for starting to turn after 170 visually inspecting all trials. 171 We updated the start and end time points to segment all signals from the bicycle and rider’s IMUs and 172 the steering angle sensors. To reduce noise and high-frequency components in the signals, we applied a 173 low-pass Butterworth filter with a cutoff frequency of 25 Hz and a second-order Butterworth filter on the 174 roll rate and steer rate signals. We also detrended the resulting filtered signals by subtracting their mean 175 values. 176 We then calculated the mean absolute steering rate, roll rate, and lean rate of the time-series data over 177 each segment that represented an approximate steady-state traversal. Finally, we calculated the average of 178 two repetitions per condition to reduce the effect of randomness in balance control. 179 Note that, desirably, we wanted two repetitions per condition. However, in a few cases, due to 180 malfunctions, the system did not apply the perturbation, resulting in trials being labeled as not disturbed. 181 For the analysis, we reported the average of the maximum number of repetitions per condition. If subjects 182 had no trials performed for a particular condition, we excluded them from the analysis. For illustration 183 purposes, we included representative time series of bicycle motion data of all conditions in multi-task 184 scenario in Appendix 1, Figure S5. 185 Bicycle’s steering motion; steering rate 186 We quantified the steering rate by the mean absolute steering rate in (deg/s). The steering rate is the 187 rate of change of steering angle around the steer axis δ(Appendix 1, Figure S6). Higher steering rate 188 correlates to the total (rider +motor) steer control effort to stabilize the vehicle. Since we have not 189 measured the rider’s steering effort separately, the results here is a summation of the rider and the steer 190 motor’s contribution to steering motion. We assumed that a decreased steering rate indicates that the rider 191 needs to put less steering effort to maintain the bicycle’s balance control, in those cases when the balance 192 assist system is switched on. If the steering rate is higher in the condition where the balance assist is on 193 compared to when it is off, we cannot draw a strong conclusion on the rider’s effort in motion control. 194 Bicycle’s lateral motion; roll rate 195 We quantified the bicycle’s lateral balance control by the mean absolute roll rate of the bicycle. The roll 196 rate is the rate of change of angle of bicycle rear frame in lateral direction (YZ plane) or around the X (or 197 forward) axis (Appendix 1, Figure S6). A lower roll rate indicates that the system is better damped and 198 there is less oscillation around the vertical axis when the system is subjected to internal (noise in motor 199 control) or external disturbances (steer motor). 200 Rider’s lateral motion; lean rate 201 The IMU’s Y axis was aligned with the participant’s spine, X axis was vertical to Y in the frontal plane, 202 and Z axis was vertical to Y axis in the sagittal plane (Appendix 1, Figure S1). We evaluated the rider’s 203 postural balance control by the lean rate defined as the rate of change of torso angle in Shimmer IMU 204 XY plane or around the Z axis relative to the Earth. A well-behaved closed loop system will damp out 205 disturbances quickly and not oscillate too much. Therefore, higher lean rate means that the rider has more 206 oscillation and the rider-bicycle closed loop system is not damping the disturbances optimally. 207 Statistics 208 To evaluate the effects of the Balance assist system, Disturbances, and Aging on dependent variables 209 (steering, roll, and rider’s lean rate), we performed repeated measures ANOV A with Balance assist (on/off) 210 and Disturbances (on/off) as within-subject factors and Aging as a between-subjects factor. In the case of 211 5/12 a significant main effect, we investigated the interactions of effects, and if significant, post-hoc ANOV A 212 was performed to test the effect of Balance assist on affected variables. 213 To evaluate the effects of Scenarios (multi-tasking) we performed the repeated measures ANOV A on 214 dependent variables with Balance assist (on/off) and Scenarios (single-task/multi-task) as within-subjects, 215 and Aging as a between-subjects factors on Undisturbed trials. Since the duration of disturbances was 216 different in single- and multi-task cycling, to evaluate the effect of Scenarios, the disturbed trials were 217 excluded for a fair comparison between two scenarios. 218 Finally, in case of effectiveness of the balance assist in improving the outcome measures, to gain 219 insights into how the balance assist system affects each age group separately, we conducted a simple main 220 effects analysis. We divided the data by age group and performed an ANOV A on each group to examine 221 the effect of the balance assist system on the roll rate and steer rate. 222 We performed the statistical analysis in JASP (University of Amsterdam, The Netherlands) version 223 0.16, and p <0.05 was considered significant. 224 RESULTS 225 The results in Table 1 and 2, show the effects of Balance assist system, Disturbances and Aging on 226 bicycle steering rate, roll rate, and the rider’s lean rate. Table 3 shows the effect of Balance assist 227 system, Scenarios (multi-tasking) and Aging on bicycle steering rate, roll rate, and the rider’s lean rate for 228 undisturbed conditions. 229 Table 1. The effects of Balance assist, Disturbances, and Aging on bicycle roll and steering and on rider’s lean rate in single-task cycling. Scenario 1; Single-task Steering rate Roll rate Lean rate F(1,30) p F(1,30) p F(1,17) p Balance assist 39.273 <.001 47.235 <.001 1.344 0.262 Disturbances 31.198 <.001 11.573 0.002 0.509 0.485 Age 2.04 0.163 6.675 0.015 2.739 0.116 Balance assist ∗Age 0.967 0.333 1.58 0.218 0.108 0.747 Disturbances ∗Age 1.606 0.205 1.398 0.246 0.142 0.711 Balance assist ∗Disturbances 0.348 0.560 0.853 0.363 0.117 0.737 Balance assist ∗Disturbances ∗Age 1.406 0.245 0.37 0.548 0.055 0.818 Table 2. The effects of Balance assist, Disturbances, and Aging on bicycle roll and steering and on rider’s lean rate in multi-task cycling. Scenario 2; Multi-task Steering rate Roll rate Lean rate F(1,26) p F(1,26) p F(1,17) p Balance assist 10.556 0.003 21.377 <.001 0.293 0.595 Disturbances 116.635 <.001 60.394 <.001 2.152 0.161 Age 3 .208 0 .085 3 .487 0 .073 3 .020 0 .100 Balance assist ∗Age 0 .975 0 .333 0 .03 0 .864 0 .189 0 .669 Disturbances ∗Age 0 .534 0 .471 2 .365 0 .136 2 e−6 0 .999 Balance assist ∗Disturbances 1 .048 0 .315 2 .101 0 .159 0 .076 0.786 Balance assist ∗Disturbances ∗Age 2 .281 0 .143 1 .904 0 .179 0 .076 0.786 Effects of Balance assist system, Disturbances, and Age per scenario 230 Effects of the Balance assist system, Disturbances, and Age in single-task cycling 231 In single-task cycling, a significant effect of the Balance assist system and Disturbances was found on the 232 steering rate, without any interaction between main factors. The results indicate that the steering rate was 233 higher in disturbed compared to undisturbed cycling (t = 5.586, p <0.001, Cohen’s d = 0.987), and that 234 activation of the Balance assist system, regardless of age or disturbances, significantly reduced steering 235 rate (t = - 6.267, p <0.001, Cohen’s d = - 1.108; Figure 3, Table 1). 236 6/12 Table 3. The effects of Balance assist, Scenarios, and Aging on bicycle roll and steering and on rider’s lean rate in undisturbed trials. Single- vs. Multi-task Steering rate Roll rate Lean rate F(1,26) p F(1,26) p F(1,15) p Balance assist 34.681 <.001 38.275 <.001 0.211 0.652 Scenario 19.678 <.001 14.041 <.001 0.514 0.484 Age 1.972 0.172 6.686 0.016 2.217 0.157 Balance assist ∗Age 0.064 0.803 1.634 0.212 0.248 0.626 Scenario * Age 2.054 0.164 0 .414 0.525 0.239 0.632 Balance assist ∗Scenario 0.299 0.589 0.10 0.921 1.114 0.308 Balance assist ∗Scenario ∗Age 0.143 0.708 0.097 0.758 0.010 0.923 Figure 3. The effect of the Balance assist system on roll and steering rate in Single-task scenario. Bar chart with error bars show the mean and standard deviation of the roll rate and steer rate in disturbed and not disturbed conditions in presence and absence of the balance assist system in both age groups. Regarding bicycle roll rate in single-task cycling, significant effects of the Balance assist system, 237 Disturbances, and Age without any interactions between main effects were observed, (Figure 3, Table 238 1). Post hoc showed that roll rate was higher in disturbed compared to undisturbed cycling, and in older 239 compared to younger adults (t = 3.402, p = 0.002, Cohen’s d = 0.601, and t = 2.584, p = 0.015, Cohen’s d 240 =0.457, respectively). The balance assist system significantly reduced the roll rate (t = - 6.873, p <0.001, 241 Cohen’s d = - 1.215). 242 In straight line cycling there was not a significant effect of Age or Disturbances nor Balance assist on 243 rider’s lean rate (Table 1). 244 Effects of the Balance assist system, Disturbances, and Age in multi-task cycling 245 There was no significant effect of Aging on steering rate (Figure 4, Table 2). However, there were effects 246 of Balance Assist and Disturbances on steering rate in multi-task cycling. While steering rate was higher 247 in disturbed compared to undisturbed cycling (t = 10.566, p <0.001, Cohen’s d = 1.997), activation of 248 the Balance assist system, regardless of age or disturbances, significantly reduced steering rates compared 249 to deactivation (t = -3.249, p = 0.003, Cohen’s d = -0.614). 250 The analysis of roll rate in multi-task cycling revealed significant strong effects of Disturbances and 251 the Balance assist system on bicycle roll rate, with no significant effects of Age or any interaction between 252 7/12 Figure 4. The effect of the Balance assist system on bicycle’s roll and steering rate in Multi-task scenario. Bar chart with error bars show the mean and standard deviation of the roll rate and steer rate in disturbed and not disturbed conditions in presence and absence of the balance assist system in both age groups. the main factors (Figure 4, Table 2). Post-hoc analyses showed higher bicycle roll rates in disturbed 253 compared to undisturbed cycling (t = 7.771, p <0.001, Cohen’s d = 1.469), while activation of the 254 Balance assist system resulted in significantly lower bicycle roll rates, regardless of age or disturbances (t 255 = -4.623, p ¡ 0.001, Cohen’s d = -0.874). 256 In multi-task cycling, there was not a significant effect of Age or Disturbances nor Balance assist 257 system on riders’ lean rate (Table 2). 258 Effects of Balance assist system, Scenarios, and Age (undisturbed cycling) 259 There was no significant effect of Aging, but there were significant effects of Scenario and the Balance 260 assist system on the bicycle’s steering rate (Table 3), without any interactions between the main factors. 261 Post-hoc analysis showed that the steering rate during multi-task cycling was higher than during single- 262 task cycling (t = 4.436, p <0.001, Cohen’s d = - 0.838), and that the steering rate was lower when the 263 balance assist system was activated, regardless of scenario (t = -5.889, p <0.001, Cohen’s d = - 1.113). 264 Furthermore, there were significant effects of Aging, Scenario, and the Balance assist system on 265 the bicycle’s roll rate (Table 3). Post-hoc analysis showed that the roll rate was higher in older adults 266 compared to young adults (t = 2.586, p = 0.016, Cohen’s d = 0.489), and higher during multi-task scenarios 267 compared to single-task scenarios (t = 3.747, p <0.001, Cohen’s d = 0.708). Regardless of Age and 268 Scenario, the bicycle’s roll rate was lower when the balance assist system was activated, compared to 269 when it was deactivated (t = - 6.187, p <0.001, Cohen’s d = - 1.169). 270 There was not any effect of Aging, Scenario, or Balance assist system on rider’s lean rate (Table 3). 271 272 DISCUSSION 273 At low forward speeds, a bicycle becomes very difficult to balance because the time-to-double of a 274 bicycle’s unstable motion can be as brief as 0.3 seconds (Hess et al., 2012). This difficulty is reflected 275 in the very large motion variability observed during straight ahead cycling at low speeds (Moore et al., 276 2010). Applying external disturbances inevitably may cause growth in motion variability. 277 We aimed to reduce motion variability and thus rider control effort at low forward speed by intro- 278 ducing balance assisting torque. We expected reduced motion variability based on the reduced need 279 8/12 for compensatory behavior (Alizadehsaravi et al., 2021) in the presence of assistive technology, as the 280 bicycle states are known to the control unit. We designed and implemented the ‘steer-into-the-fall’ control 281 algorithm that laterally stabilizes the bicycle at low forward speeds thus rejecting small perturbations. 282 We evaluated the system’s effectiveness (regarding the lateral motion and steering effort) in response to 283 real-life challenges experienced by the rider-bicycle system. We found that the balance assist decreased 284 the steering rate (effort) and damped the lateral motion (roll rate) in all conditions in both age groups, 285 indicating that less control effort may be required to steer and a better balance can be achieved on the 286 bicycle using the balance assist bicycle. The rider’s lean rate did not change with age, disturbances, or 287 balance assist, and this could be caused by the minimum role of the upper body to optimally maintain or 288 regain the bicycle’s balance control with steering action when steer and lean are available (Sharp, 2008). 289 Overall, our results suggest that balance assist system can reduce unnecessary motions at low forward 290 speed, during disturbances and multi-tasking and has potential to increase safety in challenging balancing 291 conditions. 292 Balance assist and Aging 293 Our study found a significant effect of aging on roll rate and a trend towards higher steering rates among 294 older participants, indicating reduced ability to control the bicycle’s rolling motion and suggesting that 295 age may impact bicycle balance control. Regardless of age, our results showed improved bicycle balance 296 control when the balance assist system was activated. 297 Recent research suggests that aging impacts the brain’s capacity for processing sensory inputs into 298 motor actions (Moulton et al., 2022), and our findings suggest that the balance assist system may assist 299 older cyclists by processing the bicycle’s states earlier than would occur in their central nervous system. 300 This leads to a faster control action of damping lateral motion in the ‘older rider-bicycle’ system with 301 balance assist system activated compared to when deactivated. Simple main effect tests showed that the 302 balance assist system reduced roll rate in both single-task and multi-task cycling, with a greater effect on 303 older cyclists roll rate (single-task; Older: F = 51.547 , P <0.001, Young: F = 10.386, p = 0.007 and 304 multi-task; Older: F = 14.113 , p = 0.002, Young: F = 8.355, p = 0.014). Furthermore, e-bikes reduce 305 the physical effort for propulsion, but do not provide fatigue reduction in steering and balancing task. 306 While steering in the long term may lead to fatigue and increase the risk of fall, especially in older cyclists 307 (Weavil et al., 2018), the decreased steering effort using balance assist system is promising to reduce the 308 risk of fall. The balance assist system assisted older adults more than young in reducing their steering 309 effort (steering rate) in single-task (single-task; Older: F = 44.759 , P <0.001, Young: F = 8.675, p = 310 0.011), and all undisturbed trials among both scenarios (Older: F = 42.335 , P <0.001; Young: F = 9.226, 311 p = 0.01). 312 In the context of combination of multi-tasking and disturbances, older and younger cyclists exhibited 313 similar cycling behavior with respect to roll and steering rates. Although the balance assist system reduced 314 these variables in both age groups, the reduction in roll rate was more significant in older adults than in 315 younger adults. However, the reduction in steering rate in older cyclists was not statistically significant, 316 whereas it was significant in younger cyclists (Older: F = 4.160, P = 0.061; Young: F = 5.484, p = 317 0.037). This could be due to delayed steering interruption of older adults in combination of multi-tasking 318 (distraction) and disturbances, and a longer learning period might help them adapt to the system. 319 The increased number of single-actor bicycle crashes due inadequate balance control, especially in 320 older cyclists, suggests that our system could induce balance control closer to young cyclists, potentially 321 lead to safer cycling and reduce the number of age-related accidents. However, future research with larger 322 sample sizes, various manoeuvres, and longer duration of cycling may be needed to confirm this trend 323 and determine its significance. 324 Balance assist and Disturbances 325 Internal and external disturbances are challenges that cyclists encounter during cycling, being due to 326 internal sensory and motor organs noise or environmental factors. Results showed that the disturbances 327 increased the roll rate and reduced the lateral balance control authority. We found a reduced roll rate, 328 which indicates an enhanced rider’s lateral ability to control the bicycle’s rolling motion, when using 329 balance assist system. 330 In addition, disturbances also caused an increase in the steering rate (effort), in which balance assist 331 system proved to be effective to decrease the steering rate. This indicates that using the balance assist 332 system less steering action is needed to regain balance control after being subjected to disturbances. This 333 9/12 could be beneficial for cyclists in the long term, as it can reduce physical fatigue. In addition, it may 334 also foster a healthier lifestyle by allowing people to ride for longer distances. Our findings suggest 335 that regardless of age and disturbances, amount of the steering actions to reach the perceived stability 336 decreased by using the balance assist system. 337 Balance assist and Multi-tasking 338 In multi-task cycling, the steering rate (effort) and roll rate significantly increases, reflecting the higher 339 cognitive and motor control demand of the additional tasks. Balance assist system showed to improve 340 the lateral balance control and steering effort in multi-task cycling. Overall, our results suggest that 341 multi-tasking increases the motion variability in cycling, while they are unavoidable in real-life situations, 342 balance assist system can potentially improve the balance control and reduces the risk of undesired motion. 343 Cyclists and especially older participants with lower cognitive and physical fatigue threshold could benefit 344 from balance assist bicycle in long distance and duration riding. 345 Balance assist and cyclists’ lateral balance control; lean rate 346 We did not observe any effect on the rider’s lean rate. It might be due to the rider’s choice not to lean when 347 they can also steer to maintain the bicycle’s balance, aligning with optimal control predictions (Sharp, 348 2008). Additionally, prior observations (Kooijman et al., 2009) and (Moore et al., 2011) give evidence 349 that the rider keeps their upper body quite inertially stationary in straight riding even if the roll motion 350 of the bicycle has relatively larger variability, i.e., it is much easier to roll the low inertia bicycle than 351 the rider along with it for control purposes. This could explain the lack of lean rate changes we observe 352 in this study. Alternatively, ineffectiveness of the balance assist system on the lean rate (torso lateral 353 motion) could be explained by the fact that the balance assist controller does not measure or estimate the 354 rider’s state. While the controller succeeded in keeping the bicycle in an upright position with less lateral 355 oscillation in presence of the balance assist system, it is not clear whether the riders were able to accept 356 the control action induced by the steering motor without over-reacting. Therefore, further investigation is 357 necessary to understand the rider’s perception and intention. 358 Limitation and suggestion 359 There are some limitations in our study to note. We collected the older participants’ data at a parking 360 lot at the Den Haag Gazelle User Experience Center and the young participants at a parking lot at TU 361 Delft where the surfaces of the road were slightly different (asphalt vs. cycling path bricks). However, 362 collecting all data per participant in the same situation, eliminated the risk of false results. Note that 363 the conclusion about balance assist system and aging was drawn by comparing the absolute changes 364 in variables [variables activated - variables deactivated] per age group, so false results – differences in 365 bicycle balance control between age groups – due to different road surfaces can be ruled out. 366 Moreover, in human-robot interactions, ‘trust’ is seen as a key to improve the operations and a feeling 367 of safety (Goillau et al., 2003). Therefore, it is expected that when adapting to new assistive technologies, 368 the positive effect of the assistive system is more pronounced when riders trust the technology and yield 369 to the generated steering torque by the steering motor. Since there is no direct measurement of the rider’s 370 effort, again we cannot draw a strong conclusion whether that was by choice or the rider’s put enough 371 effort in optimizing the lateral motion variability. However, we have asked riders to perform trials in a 372 consistent manner, therefore the variation in effort is not expected per participant and the results are due 373 to balance assist action, rather than changes in riders’ action. 374 We observed that 5 out of 18 older adults (all males) failed at least once in performing the first 375 multi-task trial. In future studies estimating the lateral position of the bicycle and calculating the deviation 376 of the rider-bicycle from the straight-line is suggested. It might be interesting to study the effect of gender 377 on multi-task cycling to address adequate regulations for male older cyclists. 378 One of the common issues among our older participants was having a stiff neck which would not 379 allow them to look over the shoulder without using their torso. The problem with that would be a 380 higher variation of motion in older participants while their range of motion is limited compared to young 381 participants and that influences the motion coordination and lateral balance. A small side mirror added to 382 older participants’ bicycles will potentially help to eliminate some of the hazards, but careful adaptation 383 in identifying direction and distance of motion of other objects through the mirror is required. 384 10/12 CONCLUSIONS 385 Aging, disturbances, and multi-tasking are real-life challenges, and our study demonstrated that they can 386 negatively affect cycling lateral balance and steering motion. Our balance assist system was shown to 387 be effective in improving lateral balance by reducing lateral motion (indicated by the reduced roll rate) 388 and reducing steering effort (indicated by the reduced steering rate). These effects were observed not 389 only in single-task cycling but also in multi-task cycling, and in both age groups, both in the presence 390 and absence of disturbances. The effects of the balance assist system were more significant among older 391 cyclists in the majority of conditions, except for when disturbance and multi-tasking were combined. To 392 further improve the effectiveness of the balance assist system in such a condition, adding human motion 393 detection and estimation to the control algorithm could be considered. 394 ACKNOWLEDGMENTS 395 This study is funded by Dutch Research Council, Nederlandse Organisatie voor Wetenschappelijk 396 Onderzoek (NWO), under the Citius Altius Sanius program and in collaboration with Bosch eBike 397 Systems and Royal Dutch Gazelle. Authors would like to thank the individuals who participated in the 398 experiment. Authors would also like to thank Arend Schwab and Marco Reijne for their contribution to 399 developing the balance assist system, and David Gabriel, Felix Dauer, Oliver Maier, Maarten Pelgrim, 400 and Sierd Heida for their feedback and contribution to developing the balance assist system, and Frans 401 van der helm for their feedback throughout the study. 402