Multiple Hand Posture Rehabilitation System Using Vision-Based Intention Detection and Soft-Robotic Glove

For stroke survivors, diminished hand functions limit their ability to perform activities of daily living (ADLs). Recently, soft-robotic gloves have assisted stroke survivors in active rehabilitation by facilitating their finger movements based on intentions expressed through biosignals, such as electromyogram and electroencephalogram. In this regard, helping stroke survivors actively train multiple hand postures can improve hand functions required for ADLs. However, detecting intentions regarding multiple hand postures remains challenging, often resulting in low online classification performance. To address this, we propose a hand rehabilitation system comprising a vision-based intention detection framework and 8-degree-of-freedom soft-robotic glove. Our proposed framework, depth enhanced hand posture intention network, analyzes images and depths data observing users' arm behavior and hand-object interactions to predict intentions for multiple hand postures. The 8-degrees-of-freedom soft-robotic glove facilitates flexion and extension of individual fingers to help users perform desired hand postures. To support active rehabilitation, we operate our glove to facilitate user's finger movements when the user exerts effort to generate desired hand postures. We test our system on a real-time pick and place task involving five hand postures most commonly utilized in ADLs. Our vision-based system could predict and facilitate the desired hand postures for five healthy individuals and three stroke survivors with average accuracy of 90.4 ± 3.6% and 80.3 ± 4.6%, respectively, outperforming methods reported in previous studies.