Detection of the intention to grasp during reach movements

• Published in: Journal of rehabilitation and assistive technologies engineering Vol. 5; p. 2055668317752850
• Main Authors: de Vries, JC, van Ommeren, AL, Prange-Lasonder, GP, Rietman, JS, Veltink, PH
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2018; Sage Publications Ltd
• Subjects: Machine learning; Special Collection: Wearable Technologies for Active Living and Rehabilitation; Stroke; User services; soft-robotic glove; support vector machine; Assistive technology; grasp intention detection; machine learning; stroke; inertial sensing
• ISSN: 2055-6683; 2055-6683
• DOI: 10.1177/2055668317752850

Introduction
Soft-robotic gloves have been developed to enhance grip to support stroke patients during daily life tasks. Studies showed that users perform tasks faster without the glove as compared to with the glove. It was investigated whether it is possible to detect grasp intention earlier than using force sensors to enhance the performance of the glove.
Methods
This was studied by distinguishing reach-to-grasp movements from reach movements without the intention to grasp, using minimal inertial sensing and machine learning. Both single-user and multi-user support vector machine classifiers were investigated. Data were gathered during an experiment with healthy subjects, in which they were asked to perform grasp and reach movements.
Results
Experimental results show a mean accuracy of 98.2% for single-user and of 91.4% for multi-user classification, both using only two sensors: one on the hand and one on the middle finger. Furthermore, it was found that using only 40% of the trial length, an accuracy of 85.3% was achieved, which would allow for an earlier prediction of grasp during the reach movement by 1200 ms.
Conclusions
Based on these promising results, further research will be done to investigate the possibility to use classification of the movements in stroke patients.