Human-In-The-Loop Optimization of A Wearable Robot Using Foot Pressure Sensors

Background: Individuals with below-knee amputation (BKA) experience increased physical effort when walking, and the use of a robotic ankle-foot prosthesis (AFP) can reduce such effort. Our prior study on a robotic AFP showed that walking effort could be reduced if the robot is personalized to the wearer. The personalization is accomplished using human-in-the-loop (HIL) optimization, in which the cost function is based on a real-time physiological signal indicating physical effort. The conventional physiological measurement, however, requires a long estimation time, hampering real-time optimization due to the limited experimental time budget. In addition, the physiological sensor, based on respiration uses a mask with rigid elements that may be difficult for the wearer to use. Prior studies suggest that a symmetry measure using a less intrusive sensor, namely foot pressure, could serve as a metric of gait performance. This study hypothesized that a function of foot pressure, the symmetric foot force-time integral, could be used as a cost function to rapidly estimate the physical effort of walking; therefore, it can be used to personalize assistance provided by a robotic ankle in a HIL optimization scheme. Methods: We developed a new cost function derived from a well-known clinical measure, the symmetry index, by hypothesizing that foot force-time integral (FFTI) symmetry would be highly correlated with metabolic cost. We conducted experiments on human participants (N = 8) with simulated amputation to test the new cost function. The study consisted of a discrete trial day, an HIL optimization training day, and an HIL optimization data collection day. We used the discrete trial day to evaluate the correlation between metabolic cost and a cost function using symmetric FFTI percentage. During walking, we varied the prosthetic ankle stiffness while measuring foot pressure and metabolic rate. On the second and third days, HIL optimization was used to find the optimal stiffness parameter with the new cost function using symmetric FFTI percentage. Once the optimal stiffness parameter was found, we validated the performance with comparison to a weight-based stiffness and control-off conditions. We measured symmetric FFTI percentage during the stance phase, prosthesis push-off work, metabolic cost, and user comfort in each condition. We expected the optimized prosthetic ankle stiffness based on the newly developed cost function could reduce the energy expenditure during walking for the individuals with simulated amputation. Results: We found that the cost function using symmetric foot force-time integral percentage presents a reasonable correlation with measured metabolic cost (Pearson’s R > 0.62). When we employed the new cost function in HIL ankle-foot prosthesis parameter optimization, 8 individuals with simulated amputation reduced their cost of walking by 15.9% (p = 0.01) and 16.1% (p = 0.02) compared to the weight-based and control-off conditions, respectively. The symmetric FFTI percentage for the optimal condition tended to be closer to the ideal symmetry value (50%) compared to weight-based (p = 0.23) and control-off conditions (p = 0.04). Conclusion: This study suggests that foot force-time integral symmetry using foot pressure sensors can be used as a cost function when optimizing a wearable robot parameter.