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Humans can inherently adapt their gait pattern in a way that minimizes the metabolic cost of transport, or walking economy, within a few steps, which is faster than any known direct physiological sensor of metabolic energy. Instead, walking economy may be indirectly sensed through mechanoreceptors that correlate with the metabolic cost per step to make such gait adaptations. We tested whether velocity feedback from tibialis anterior (TA) muscle fascicles during the early stance phase of walking could potentially act to indirectly sense walking economy. As participants walked within a range of steady-state speeds and step frequencies, we observed that TA fascicles lengthen on almost every step. Moreover, the average peak fascicle velocity experienced during lengthening reflected the metabolic cost of transport of the given walking condition. We observed that the peak TA muscle activation occurred earlier than could be explained by a short latency reflex response. The activation of the TA muscle just prior to heel strike may serve as a prediction of the magnitude of the ground collision and the associated energy exchange. In this scenario, any unexpected length change experienced by the TA fascicle would serve as an error signal to the nervous system and provide additional information about energy lost per step. Our work helps provide a biomechanical framework to understand the possible neural mechanisms underlying the rapid optimization of walking economy. 

For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system.