Abstract
This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.
Highlights
Electromyography (EMG) has been applied to various fields
This phenomenon was found for all five subjects during the consecutive stepping test. This kind of phenomenon doesn’t appear in the continuous motion test. This phenomenon indicates that the subject must provide more effort to achieve the task in the consecutive stepping text than in the continuous motion text and the fluctuation in EMG signals during consecutive stepping test reflects upon this effort
This paper proposed an upper limb elbow joint representation method that uses only single-channel EMG signals
Summary
Fukuda et al [1] used EMG signals to control a manipulator They adopted a statistical neural network, named the log-linearized Gaussian mixture network, to achieve robust discrimination against differences among individuals, electrodes locations, and time variations caused by fatigue or sweat. EMG signals reflect the level of muscle activation, and can be used to predict or recognize human motion [3,4,5,6,7] This kind of technology is especially useful for the physically handicapped person, as applied by Fukuda [1]. Ajoudani et al [11] used EMG signals from eight muscles around the operator’s arm to derive stiffness information, which was sent with the position command to a slave robot to achieve tele-impedance control
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