EMG-Powered Motion Analysis and Upper-Limb Muscle Training Based on Hexagon Output Distribution

The number of subjects facing difficulties in muscle strength recovery and rehabilitation amid elderly people, injured athletes and post-stroke patients, is still rising. This situation brings a heavier burden on the society through financial and medical expenses in hospitals and in specialised centers, where clinicians and physiotherapists are involved. In this medical field, many researchers have been focusing on the development of human-aided and robot-aided rehabilitation exercises, with the main aim of helping the affected people to go back to a normal muscle condition that allows them to perform activities of daily life (ADL) independently. However, some issues have to be taken into account while designing exercises for rehabilitation and training purposes. In fact, it is still not clear when and for how long it is better to carry out the rehabilitation process, and especially how to design subject-specific exercises in order to train and re-educate only the damaged muscles. Hence, in this research, firstly we have focused on how to evaluate and assess the damage and the overall condition in the upper limb muscles of a generic subject without using invasive methods, and then consequently design muscle-specific training exercises that can be carried out at home. In the proposed framework, the maximum output force at the tip of the hand is evaluated by a 6-axis force sensor and secondarily displayed through a Hexagon Distribution (HD), which shows the target muscles and is finally used with the Electromyography (EMG) signals to develop a constrained path that the patient has to follow with the hand, to train only the affected muscles.