Mechanomyography (MMG) is the muscle surface oscillations that are generated by the dimensional change of the contracting muscle fibers. Because MMG reflects the number of recruited motor units and their firing rates, just as electromyography (EMG) is influenced by these two factors, it can be used to estimate the force exerted by skeletal muscles. The aim of this study was to demonstrate the feasibility of MMG for estimating the elbow flexion force at the wrist under an isometric contraction by using an artificial neural network in comparison with EMG. We performed experiments with five subjects, and the force at the wrist and the MMG from the contributing muscles were recorded. It was found that MMG could be utilized to accurately estimate the isometric elbow flexion force based on the values of the normalized root mean square error (NRMSE = 0.131 ± 0.018) and the cross-correlation coefficient (CORR = 0.892 ± 0.033). Although MMG can be influenced by the physical milieu/morphology of the muscle and EMG performed better than MMG, these experimental results suggest that MMG has the potential to estimate muscle forces. These experimental results also demonstrated that MMG in combination with EMG resulted in better performance estimation in comparison with EMG or MMG alone, indicating that a combination of MMG and EMG signals could be used to provide complimentary information on muscle contraction.

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This paper proposes an improved robust interacting multiple model (RIMM) algorithms with modeling uncertainties for maneuvering target tracking with changing dynamics. To mitigate the effects of the modeling uncertainty, a compensation step is introduced to adjust the degree of dependence of the filtering on the system or the measurement model based on the orthogonality principle between the state estimation error and innovation sequence of the subfilter model in the RIMM algorithm. By relying on the compensation parameter, the proposed algorithm fully utilizes the useful information in the innovation sequence and reduces the impact of system model error. The numerical simulation and car-mounted experiments using time difference of arrival (TDOA) measurements of the maneuvering target tracking with changing dynamics are conducted to verify the effectiveness of the proposed RIMM algorithm. Compared with the conventional approaches, the proposed RIMM algorithm results in a remarkable improvement in the state estimation accuracy and small bias while improving the consistency of the filter. INDEX TERMS Robust interacting multiple model, maneuvering target tracking, modeling uncertainties, orthogonality principle, unscented Kalman filter, extended Kalman filter.

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