Motion Path Design for Specific Muscle Training Using Neural Network

Specific muscle training is expected to be used for efficient rehabilitation and care prevention. In this paper, we propose algorithms for designing a motion path capable of strengthening specific muscles. By using the proposed algorithms, it is possible to design a motion path maximizing the activity of an agonist muscle and minimizing that of other muscles. For training, the load is applied by using a 2-link arm. EMG signal is measured during a training experiment, and the degree of muscular revitalization is evaluated by the amplitude of EMG signal. Finally, the effectiveness of the proposed approach is demonstrated through experiments. 1. Introduction In recent years, in the context of the emergence of population ageing as a social issue in developed countries, the importance of regaining muscle strength for care prevention has become increasingly apparent. Prolonged immobility induces muscle weakness, which affects activities of daily living (ADLs) directly. Much research is being done on rehabilitation robotics that is pertinent to strength training. Lum et al. indicated that, compared with conventional therapy techniques, robot-assisted training is more efficient for improving muscle strength and path-following capability [1]. For lower limb rehabilitation, Akdo？an and Adli developed a therapeutic exercise robot that enables rehabilitation for spinal cord injury in diverse ways, including both isotonically and isometrically [2]. Such research aims to regain a muscle strength of an entire arm or leg. Therefore, the robots are unable to apply a load to specific muscles. However, the degree of muscle weakness differs according to each muscle. Thus, the application of a load to specific muscles that require strengthening is expected to lead to more efficient and safer training. In the authors' previous research, a method of estimating muscle force or level of muscle activation was proposed [3]. Though the objective of the research was to strengthen a muscle by isometric exercise, an isotonic exercise is more effective for ADLs training. The purpose of the present work is to develop an algorithm for designing a motion path capable of strengthening specific muscles for isotonic exercise. The method proposed in previous research does not consider the coordinated motion of an antagonistic muscle. However, when doing an exercise, an antagonistic muscle works to increase the stiffness of each joint, such as a shoulder or an elbow. A method of estimating muscle activity should consider the coordinated motion. In this paper, a neural network is used since it