A general principle of human movement is that our nervous system is able to learn optimal coordination strategies. However, how our nervous system performs this optimization is not well understood. Here we design, build, and test a mechatronic system to probe the algorithms underlying optimization of energetic cost in walking. The system applies controlled fore-aft forces to a hip-belt worn by a user, decreasing their energetic cost by pulling forward or increasing it by pulling backward. The system controls the forces, and thus energetic cost, as a function of how the user is moving. In testing, we found that the system can quickly, accurately, and precisely apply target forces within a walking step. We next controlled the forces as a function of the user’s step frequency and found that we could predictably reshape their energetic cost landscape. Finally, we tested whether users adapted their walking in response to the new cost landscapes created by our system, and found that users shifted their step frequency towards the new energetic minima. Our system design appears to be effective for reshaping energetic cost landscapes in human walking to study how the nervous system optimizes movement.
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