Computer vision-based hand deviation exercise for rehabilitation

Abstract

Computerized monitoring of the home based rehabilitation exercise has many benefits and it has attracted considerable interest among the computer vision community. Nowadays, many rehabilitation systems are proposed, most of the targeted disability is for stroke patient. Some of patient or user just wants to take certain part for rehabilitation. Therefore, this paper is focusing on hand rehabilitation system. The importance of the rehabilitation system is to implement the specific exercise for the specific requirements of the patients that needs rehabilitation therapy. This paper presents the specific hand rehabilitation system using computer vision method. The specific hand rehabilitation implemented in this system is a hand deviation exercise. This exercise is benefited to improve the mobility of the hand and reduce the pain. The hand tracking and finger detection method are used in this hand rehabilitation system. The result of the exercise can be used as a training data for the analysis of the injured hand recovery and healing process.

An Interactive Application for Hand Muscle Fatigue Relief Based on Gesture Recognition

“…It is studied that hand manipulation exercises can promote local motor enhancement, effectively enhancing muscle strength, flexibility, and coordination, while reducing discomfort related to prolonged use of electronic devices. In an existing study, Shuo Jiang demonstrated that repetitive exercise is the best strategy for restoring muscle activation, enhancing muscle strength, and avoiding hand fatigue [8].…”

Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods

“…These methods need only low-cost equipment that is easy to set up to monitor the human body. However, without control over specific conditions such as illumination and shadows, their detection accuracy may be reduced [10][11][12][13]. If the distance between the camera and an individual is inappropriate, the image information might be distorted owing to motion blur [14].…”

“…Based on non-wearable sensors, there are two main popular classes of methods in computer vision for detecting people in an image [10][11][12][13][14][15]. The methods in the first class are referred to as traditional methods.…”

“…However, this method requires of a large various of object categories and labeling a dataset with feasible occlusions of every category. In term of occluded human image, if the computer vision techniques [10][11][12] can detect some parts of the body of human such as hand or face, these body parts may be possible to determine the position of the lower-body from the locations of the detected body parts by referencing anthropometric data [21].…”