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Simha, S.

Publications and source records attributed to Simha, S..

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Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split-belt treadmill walking

In everyday tasks such as walking and running, we exploit the work performed by external sources such as gravity to reduce the work performed by muscles. There has been considerable recent effort to design devices capable of performing mechanical work to improve walking function or reduce effort. The success of these devices relies on the user adapting their natural control strategies to take advantage of assistance provided by the device. Although locomotor adaptation is central to this process, the study of adaptation is often done using approaches that on the surface, seem to have little in common with the use of external assistance. Here, we show that one of the most common approaches for studying this process, which is adaptation to walking on a split-belt treadmill, can be understood from a perspective in which people learn to take advantage of mechanical work performed by the treadmill. During adaptation, people systematically adjust their step lengths, defined as the distance between the feet at heel strike, from one step to the next. Initially, the step length on the slow belt is longer than the step length on the fast belt, measured as a negative step length asymmetry, but people naturally reduce this asymmetry with practice. Here, we demonstrate that these modifications of step length asymmetry allow people to extract positive work from the treadmill belts to reduce the positive work performed by the legs and simultaneously reduce metabolic cost. Moreover, we show that walking with a positive step length asymmetry minimizes metabolic cost, and people prefer to walk in this manner when allowed to select their walking pattern. Together, our results suggest that split-belt adaptation can be interpreted as a process by which people learn to take advantage of mechanical work performed by an external device to improve walking economy.

neuroscience