Interactive assistant tool for the evaluation of kinematic patterns and EMG signals in patients with a forearm injury

Abstract

Subjective feelings feedbacks are commonly employed by a patient during forearm rehabilitation therapy without real-time data, leading to suboptimal recovery results in some patients. Technological innovations in the field of assisted rehabilitation have enabled the evolution of real-time monitoring systems. In this paper, interactive assistant development is presented as the interface to define the relationship between the kinematics patterns and the electromyographic signals during the forearm rehabilitation routine. Leap Motion (LM) and Shimmer3 EMG sensors read the routine behavior by following the movements that appear on the software. Real-time targets are programmed to lead the necessary forearm movements that the therapist sets to determine the recovery progress. The integration of software and hardware shows a dataset basis on interaction variables such as arm velocity, arm position, performance rate, and electrical muscle pulse. The results obtained from tests show that the system works effectively within a range of movement of 9 to 88 degrees in rotation about the axes, and velocities under 190 mm/s show stable movement representation on software. Finally, the outcomes ranges show an alternative tool to evaluate patients with a forearm injury.