This study proposes a modular data glove system to accurately and reliably capture hand kinematics. This data glove system’s modular design enhances its flexibility. It can provide the hand’s angular velocities, accelerations, and joint angles to physicians for adjusting rehabilitation treatments. Three validations—raw data verification, static angle verification, and dynamic angle verification—were conducted to verify the reliability and accuracy of the data glove. Furthermore, to ensure the wearability of the data glove, 15 healthy participants and 15 participants with stroke were recruited to test the data glove and fill out a questionnaire. The errors of the finger ROMs obtained from the fusion algorithm were less than 2°, proving that the fusion algorithm can measure the wearer’s range of motion accurately. The result of the questionnaire shows the participants’ high satisfaction with the data glove. Moreover, a comparison between the proposed data glove and related research shows that the proposed data glove is superior to other data glove systems.
Lin, B-S., Lee, I-J., Chiang, P-Y., Huang, S-Y., & Peng, C-W. (2018). A Modular Data Glove System for Finger and Hand Motion Capture Based on Inertial Sensors. Journal of Medical and Biological Engineering. https://doi.org/10.1007/s40846-018-0434-6