Abstract
The accurate measurement of human joint torque is one of the research hotspots in the field of biomechanics. However, due to the complexity of human structure and muscle coordination in the process of movement, it is difficult to measure the torque of human joints in vivo directly. Based on the traditional elbow double-muscle musculoskeletal model, an improved elbow neuromusculoskeletal model is proposed to predict elbow muscle torque in this paper. The number of muscles in the improved model is more complete, and the geometric model is more in line with the physiological structure of the elbow. The simulation results show that the prediction results of the model are more accurate than those of the traditional double-muscle model. Compared with the elbow muscle torque simulated by OpenSim software, the Pearson correlation coefficient of the two shows a very strong correlation. One-way analysis of variance (ANOVA) showed no significant difference, indicating that the improved elbow neuromusculoskeletal model established in this paper can well predict elbow muscle torque.
Highlights
Human joint torque is one of the key reference indexes in rehabilitation evaluation and human-machine interaction
To verify the elbow muscle torque prediction model proposed in this paper, the elbow angle data and the muscle activation of the six muscles contained in the musculoskeletal model need to be input
The extracted muscle activation is input into the numerical model, and the muscle force and elbow muscle torque calculated by the numerical model are compared with the results calculated by OpenSim
Summary
Human joint torque is one of the key reference indexes in rehabilitation evaluation and human-machine interaction. Pontonnier and Dumont proposed a method to obtain muscle force according to the captured human motion data and established a human inverse dynamic model [6]. In recent years, many scholars have proposed the method of using sEMG signals to solve joint torque, which has been used in human upper limb elbow joint [8], index finger [9], lower limb knee joint [10], and ankle joint [4]. Meng et al used the root mean square characteristics of sEMG signals of four lower limb muscles as the input of the support vector regression model to estimate human-robot interaction force [12]. Joint torque prediction based on musculoskeletal model needs to collect a large number of motion parameters and human physiological parameters, and the process of muscle strength estimation is complex [1].
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