Extended Data Fig. 4: Optimizing assistance during real-world walking.

The exoskeleton applied speed-adaptive control, which adjusted exoskeleton assistance parameters on each step. Stride duration (t_stride) was used to estimate walking speed (v) as described in Fig. 3. While the participant walked, portable sensor data (d) were collected, which included ankle angle (θ), ankle velocity (\(\dot{{\boldsymbol{\theta }}}\)), and the control law defining exoskeleton assistance torque (C). If sufficient continuous strides (z) were not collected before the bout finished, the data were discarded and evaluation of the same control law began anew on the next walking bout. If sufficient continuous strides were collected, then data were stored for the associated control law number (n) and walking speed bin (b), selected based on the average walking speed for the collected strides. The control law number was incremented and the next control law was applied to the user. After six control laws had been applied for a given walking speed bin, forming one generation for the optimizer, the stored data were used to update the optimization parameters associated with that speed bin. When any bin performed an update, the estimate of the optimal parameter values (μ) for the other bins were also updated. Bins that were closer to convergence, indicated by a small value of the convergence parameter (σ) for that bin, were adjusted less. This approach allowed the optimizer to rapidly adapt to the participant early in the optimization, then to fine-tune the speed-specific parameters as the optimization progressed. Following the update, the optimizer selected a promising set of new control laws to be sequentially evaluated in the next generation for the associated walking speed bin.