LAUSR

This paper proposes a data glove system integrated with six-axis inertial measurement unit sensors for evaluating the hand function of patients who have suffered a stroke. The modular design of this data glove facilitates its use for stroke patients. The proposed system can use the hand’s accelerations, angular velocities, and joint angles as calculated by a quaternion algorithm, to help physicians gain new insights into rehabilitation treatments. A clinical experiment was performed on 15 healthy subjects and 15 stroke patients whose Brunnstrom stages (BSs) ranged from 4 to 6. In this experiment, the participants were subjected to a grip task, thumb task, and card turning task to produce raw data and three features, namely, the average rotation speed, variation of movement completion time, and quality of movement; these features were extracted from the recorded data to form 2-D and 3-D scatter plots. These scatter plots can provide reference information and guidance to physicians who must determine the BSs of stroke patients. The proposed system demonstrated a hit rate of 70.22% on average. Therefore, this system can effectively reduce physicians’ load and provide them with detailed information about hand function to help them adjust rehabilitation strategies for stroke patients.