ARhT: A portable Hand therapy system

Abstract

We introduce ARhT (Automated Relearning hand Therapy), a portable hand therapy system that enables a user to perform physical therapy at the comfort of their own home. This reduces rehabilitation time, enhances the user experience, reduces cost and provides accountability to physical therapy sessions. ARhT complements traditional therapy methods by interacting with the user in real time and providing the patient user friendly instructions, feedback, and progress tracking. The therapist pre-selects the hand gestures that comprise every workout and can view session information on a patient to patient basis within a standalone web application. ARhT incorporates a data acquisition subsystem which houses EMG sensors and a custom computation and communication board. The sensor data is transmitted to an Android smartphone that determines the user performance and interacts with the user through a graphical user interface. Our results show that our system recognizes hand therapy gestures with over 95% accuracy.

Comparing Dynamic Hand Rehabilitation Gestures in Leap Motion Using Multi-Dimensional Dynamic Time Warping

“…We can now illustrate how to obtain a minimum-distance warp path with an example. Given X = (1,2,4,3,5,3,2,3,2,5) that represents a query series and Y = (1,1,2,4,3,5,3,2,3,2) which represents a reference series, we can construct a cost matrix D. The minimum-distance of a warp path is traced through the matrix constituents from D(1,1) to D(M,N), highlighted in pink, as shown in Fig. 3.…”

“…ATIENTS suffering from stroke, neurological disorders, hand related injuries etc. usually need different forms of hand rehabilitation in order to recover expeditiously [1]. Most hand rehabilitation procedures are recommended to be executed with help of a physiotherapist that supervises a patient so that he or she can perform the correct hand exercises or gestures [2].…”

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

“…The algorithm finds an optimal hyper plane w.X + b = 0 that separates the training samples X 1 , ..., X m labeled using Y 1 , ...Y m such that Y i ∈ {−1, +1} [1]. The optimization problem can be expressed using (11), such that y i (w.X i + b) ≥ 1 for i = 1, ...m, where w and b define the separating hyper plane w.X + b = 0.…”

“…People suffering from neurological disorders, stroke or paralysis often require various kinds of exoskeleton rehabilitation and physiotherapy to recover quickly [11]. Even muscular or bone injuries are treated with follow-up rehabilitation.…”