**4. Discussion**

The purpose of the study was to validate the use of SRS against a 3D motion capture system and to identify ankle joint kinematics during both unexpected and expected slip and trip perturbations for fall detection. SRS was hypothesized to be a valid tool for detecting ankle joint movements during both unexpected and expected postural slip and trip perturbations. Results from the current study indicated SRS as a viable product to detect ankle joint kinematics during unexpected and expected slips and trips and potentially serve as an early fall detection device. This was evident from the observed results, indicated by a high adjusted R-square value and low RMSE in the goodness of fit model between motion capture kinematics and SRS capacitance data during slip–trip trials.

During a slip perturbation, the ankle joint moves into a PF position as the center of mass (COM) of the human body is forced outside of the standing base of support (BOS) area in the posterior direction (leaning backward) [26,28,29]. During a trip perturbation, the ankle joint moves into DF position as the COM is forced outside the BOS in the anterior direction (leaning forward) [26,28,29]. Moreover, during such postural perturbations, not having the knowledge (unexpected/unanticipated) and having the knowledge (expected/anticipated) of the perturbation influences the biomechanics of fall recovery [31–34]. Unanticipated recoveries work using feedback postural control, whereas anticipated recoveries work using feedforward postural control. Results from the current study, as demonstrated in Figures 6 and 7, support the behavior of the ankle joint during slip and trip postural perturbations. Comparison of ankle ROM change and peak, quantified by a motion capture system and capacitance change, measured by stretch sensors during these unexpected and expected slips and trips supported previous fall research conducted through motion capture technology [35,36]. Additionally, the results from the current study support previous literature regarding the feasibility of using wearable sensors for fall detection [22]. However, the primary purpose of the current study was to assess if stretch sensors could be used to identify ankle joint kinematics during such slip–trip perturbations to detect falls.

The current findings from this slip–trip study, presented here as Part III, are an extension of Part I and Part II papers from two previous studies from the current researchers on "closing the wearable gap" projects, in an attempt to develop feasible but accurate sensors using stretchable SRS for human movement monitoring from the ankle joint and above (from the ground up) [20,24]. The use of SRS was reported to produce a linear model for PF movement using a custom-built ankle joint device from Part I of "Closing the wearable gap" [20]. Additionally, testing the SRS on human participants to detect ankle joint movements compared with motion capture data was successful and reported to accurately detect ankle joint PF, DF, INV, and EVR movements using four stretch sensors from Part II of "Closing the wearable gap" [24]. The primary aim in Part II of the paper [24] was to test the soft robotic sensors for placement and orientation on the foot and ankle segment. Because the foot and ankle segmen<sup>t</sup> is a complex human joint (capable of triaxial movements), the orientation and placement/location of the sensors was crucial to ge<sup>t</sup> the accurate measurements of the movements possible. Hence, a total of 10 positions/locations and orientations were compared to identify the most desirable location for accurate movements. Additionally, due to the complexity of the foot and ankle movements, the following were the testing conditions: only isolated movements (one at a time) of ankle dorsiflexion, plantarflexion, inversion, and eversion; only in non-weight-bearing conditions, meaning not making contact with the ground, and only in one side of the foot (right side). However, in the current study, the previously developed sensors are being used for an entirely new application with fall prevention. There have been multiple studies reporting the e fficacy of using postural perturbation in studying falls for fall detection. There have also been multiple studies that have used wearable sensors for fall detection. However, to the author's knowledge, there has not been a previous study to validate the use of stretchable soft robotic sensors for fall detection (slips and trips). Additionally, the current project and paper addresses more real-life situations for falls that could be analyzed from a laboratory setting and is di fferent from our Part II paper [24] in the following methods: combined movements of the foot and ankle movements with novel movement patterns of slipping and tripping; in weight-bearing conditions replicating slips and trips both without and with the knowledge of the individual; sensors on both sides of the feet to identify any asymmetries; validate the use of soft robotic sensors for fall detection that can be applied to all populations, ranging from geriatric to athletic and from clinical to occupational, all populations who are fall prone. Finally, the focus of this project and paper was to identify if these types of stretchable soft robotic sensors could be used for fall detection by measuring ankle range of motion, as these types of sensors have not been used for this purpose previously, at least to the author's knowledge.

Subsequently, using these wearable SRS for detecting falls and potentially creating a wearable fall detection device is much needed. The current study tested the use of SRS during simulated real-life-type falls using backward (slip) and forward (trip) perturbations, both without (unexpected) and with (expected) the knowledge of the perturbation. Results from the current study supported the findings from Part I and Part II of the previous studies [20,24]. Based on the current findings, the use of SRS was found to have greater R-squared value and lower RMSE in the linear regression model, suggesting greater goodness of fit in comparing motion capture ankle joint kinematics with capacitance change from the SRS. The violin plots in Figures 9 and 10 demonstrate that a greater portion of participants produced an R-squared value of more than 0.75 (moderate to high accuracy) and a greater portion of participants produced an RMSE value of lower than 4 (minimal errors). The higher R-squared values and low RMSE were also evident when comparing all unexpected and expected slip and trip trials and across both feet as well. Results from this study indicate that the stretch sensors could be used as a feasible option in detecting falls during slips and trips, even when they are unexpected or expected and across both left and right foot–ankle segments. Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees di fference from the motion capture system (lowest RMSE = 1.06 degrees and average RMSE = 3.13 degrees for the left foot and lowest RMSE = 0.81 and average RMSE = 3.33 degrees for the right foot) and a greater than 0.60 R-squared (highest R-squared value was 0.9781 and average R-squared = 0.7658 for the left foot and highest R-squared value was 0.9832 and average R-Squared = 0.7362 for the right foot) value in the linear model, suggesting a moderate to high accuracy and minimal errors in comparing SRS with a motion capture system. The R-squared values and RMSE were also evident when comparing all unexpected and expected slip and trip trials and across both feet as well, suggesting that SRS was a feasible option to detect bilateral ankle joint movements during slip–trip perturbations, using a total of four sensors.

While motion capture technology aids assessment of the joint ROM with gold-standard precision measures [18], it is still majorly confined within a laboratory setting, with limited implications to everyday tasks. Moreover, the financial cost and time consumed are also greater with the use of laboratory-based motion capture equipment. Therefore, there is a grea<sup>t</sup> demand for alternative solutions to precisely measure joint kinematics outside of a laboratory that have lower financial and time cost and can capture day-to-day, real-life scenarios. A wearable device that can measure changes in joint ROM and limit the negative aspects of motion capture while being precise appears as a promising solution [37]. The current study's results o ffer unique findings in validating the use of wearable stretch sensors that can detect ankle joint ROM while minimizing limitations that exist with motion capture and other wearable devices for fall detection.
