Foot contact forces can be used to personalize a wearable robot during human walking.

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. The walking effort could be further reduced if the robot is personalized to the wearer using human-in-the-loop (HIL) optimization of wearable robot parameters. The conventional physiological measurement, however, requires a long estimation time, hampering real-time optimization due to the limited experimental time budget. This study hypothesized that a function of foot contact force, the symmetric foot force-time integral (FFTI), could be used as a cost function for HIL optimization to rapidly estimate the physical effort of walking. We found that the new cost function presents a reasonable correlation with measured metabolic cost. When we employed the new cost function in HIL ankle-foot prosthesis stiffness parameter optimization, 8 individuals with simulated amputation reduced their metabolic cost of walking, greater than 15% (p < 0.02), compared to the weight-based and control-off conditions. The symmetry cost using the FFTI percentage was lower for the optimal condition, compared to all other conditions (p < 0.05). 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.