The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.
Body joint movement analysis is extremely essential for health monitoring and treatment of patients with neurological disorders and stroke. Chronic hemiparesis of the upper extremity following a stroke causes major hand movement limitations. There is possibility of permanent reduction in muscle coactivation and corresponding joint torque patterns due to stroke . Several studies suggest that abnormal coupling of shoulder adductors with elbow extensors and shoulder abductors with elbow flexors often leads to some stereotypical movement characteristics exhibited by severe stroke patients . Therefore continuous and effective rehabilitation therapy is absolutely essential to monitor and control such abnormalities. There is a substantial need for home-based rehabilitation post-clinical therapy.
Source: Accurate upper body rehabilitation system using kinect – IEEE Xplore Document
Stroke patients usually have difficulties to conduct rehabilitation training themselves, due to no rehabilitation evaluation in time and dependence on doctors. In order to solve this problem, this paper proposes a motion rehabilitation and evaluation system based on the Kinect gesture measuring technology combining VR technology as well as traditional method of stroke rehabilitation. Real-time rehabilitation motion feedback is achieved by using Kinect motion capturing, customized skeleton modeling, and virtual characters constructed in Unity3D. The jitter problem of virtual characters following motion using Kinect is solved. Fidelity and interactivity of virtual rehabilitation training is improved. Our experiment validated the feasibility of this system preliminarily.
With an ageing population problem increasingly prominent, the number of hemiplegia patients is growing caused by stroke, which has a high morbidity and high mortality rate . Stroke can lead to the dysfunction of the brain central nervous, often characterized by language, cognitive or motor dysfunction , . The medical rehabilitation mechanism of stroke is based on neural plasticity theory and the theory of mirror neurons .
Source: A motion rehabilitation self-training and evaluation system using Kinect – IEEE Xplore Document