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Summary
Identifying the optimal pattern of assistive torque provided by an exoskeleton over the course of the person's walking stride is challenging. Engineers have been developing wearable devices to reduce the metabolic cost of walking for more than a century, but only in the past 4 years have groups succeeded in this, using ankle exoskeletons (1–3). Brute-force approaches can test various timing and magnitude settings of the torque pattern and identify the settings that produce the largest reduction in metabolic cost (1, 3–6). However, obtaining reliable metabolic cost data requires averaging multiple minutes of breath data, which in turn limits the number of settings that can be tested. On page 1280 of this issue, Zhang et al. (7) describe an algorithm that optimizes the entire exoskeleton torque pattern in a 1-hour iterative process with real-time metabolic cost estimations. This smart human-in-the-loop algorithm identified an optimal pattern for each participant and resulted in an average reduction of 24% compared to participants walking with the exoskeleton powered off.
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