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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.

This paper presents the elbow flexion and extension rehabilitation exercise system using marker-less Kinect-based method. The proposed exercise system is developed for the upper limb rehabilitation application that utilizes a low cost depth sensor. In this study, the Kinect skeleton tracking method is used to detect and track the joints of upper limb and then measure the angle of the elbow joint. The users perform the exercise in front of the Kinect sensor and the computer monitor. At the same time, they can see the results that displayed on the screen in real-time. The measurement of elbow joint angles are recorded automatically and has been compared to the reference values for the analysis and validation. These reference values are obtained from the normal range of motion (ROM) of the elbow. The results show the average flexion angle of the elbow joint that achieved by the normal user is 139.1° for the right hand and 139.2° for the left hand. Meanwhile, the average extension angle is 1.72° for the right hand and 2.0° for the left. These measurements are almost similar to the standard range of motion (ROM) reference values. The skeleton tracking works well and able to follow the movement of the upper arm and forearm in real-time.

Hand tracking is a common task in a gesture recognition system. Many techniques have been introduced to make successful hand tracking. In hand tracking system, most of previous works tracked the hand position using attached marker on hands. Several researchers have used a color image for skin color detection. However, using marker based need to attach marker on hands or wear gloves to make hand can be detected. When using color information, there is a need to extract many different skin colors. Furthermore, the lighting and background on the situation also need to be concerned to avoid a cluttered background that can affect the detection and tracking. This paper presents the real-time hand tracking using three dimensional (3D) data. This 3D data is coming from the Kinect sensor, which is working in real-time. 3D data from Kinect sensor is depth image data which can be used to detect and track the motion of the hand. This paper proposes hand tracking method using a hand tracker algorithm released by NiTE, hand's segmentation method, hand contour detection and center of palm detection. The hand's segmentation method consists of the ROI of the hand's area and background subtraction. The propose hand tracking algorithm is rotation invariant, since it can detect and track various rotations of hand. Additionally, it also can remove unwanted object (noise) that also moving parallelly with the hand's position.Index Terms-hand tracking, depth image, 3D data, Kinect sensor.