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  1. Abstract Background

    Improving 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.


    The 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.


    Ten 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.


    On 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.


    The 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.

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  2. Abstract B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human–robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20 Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream. 
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  3. null (Ed.)
    Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time. 
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