DOI : https://doi.org/10.18517/ijaseit.7.1.1973

Accelerometer-Based Recorder of Fingers Dynamic Movements for Post-Stroke Rehabilitation

Fajar Akhmad Dwiputra (1), Balza Achmad (2), - Faridah (3), - Herianto (4)
(1) Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
(2) Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
(3) Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
(4) Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
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How to cite (IJASEIT) :
Dwiputra, Fajar Akhmad, et al. “Accelerometer-Based Recorder of Fingers Dynamic Movements for Post-Stroke Rehabilitation”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, Feb. 2017, pp. 299-04, doi:10.18517/ijaseit.7.1.1973.
Citation Format :
Stroke is a disease that currently attracts more attention in Indonesia according to the statistics provided by the Ministry of Health of the Republic of Indonesia. This research was motivated by the shortage of physiotherapists which can not catch the increasing number of stroke patients. The therapy becomes less effective and less efficient since each therapist must handle too many patients during his/her work hours. This research has developed a device prototype that can help the therapy to measure and monitor patient exercise, especially at the final stage of rehabilitation when the patient gets therapy to move actively. The angle of the moving body parts  that can represent the ability of patient motion was measured using accelerometers. The developed prototype was in the form of a glove, equipped with an Arduino Nano and two accelerometer modules, that measures the motion of the thumb and index finger. The device was calibrated and tested to determine the characteristics of the sensors. This test showed that the gloves prototype had an accuracy of 95,8% and precision of 99,6%. The application of the prototype was carried out on four types of finger movements, namely thumb abduction-adduction, thumb flexion-extension, finger flexion-hyperextension, and finger abduction-adduction. The prototype was also tested for its ability to work in variations of direction and position of the hand.

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