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Shoes or insoles embedded with carbon fiber materials to increase longitudinal stiffness have been shown to enhance running and walking performance in elite runners, and younger adults, respectively. It is unclear, however, if such stiffness modifications can translate to enhanced mobility in older adults who typically walk with greater metabolic cost of transport compared to younger adults. Here, we sought to test whether adding footwear stiffness via carbon fiber insoles could improve walking outcomes (eg, distance traveled and metabolic cost of transport) in older adults during the 6-minute walk test. 20 older adults (10 M/10 F; 75.95 [6.01] y) performed 6-minute walk tests in 3 different shoe/insole stiffnesses (low, medium, and high) and their own footwear (4 total conditions). We also evaluated participants’ toe flexor strength and passive foot compliance to identify subject-specific factors that influence performance from added shoe/insole stiffnesses. We found no significant group differences in distance traveled or net metabolic cost of transport (P ≥ .171). However, weaker toe flexors were associated with greater improvement in distance traveled between the medium and low stiffness conditions (P = .033,r = −.478), indicating that individual foot characteristics may help identify potential candidates for interventions involving footwear stiffness modifications. 

Abstract BackgroundImproving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. ObjectiveThe purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. MethodsTen young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. ResultsOn average, the normalized moment prediction root mean square error was reduced by 14.58 % ($$p=0.012$$ $p=0.012$) and 36.79 % ($$p<0.001$$ $p<0.001$) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. ConclusionsThe proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.