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Int J Med Inform. 2018 May;113:85-95. doi: 10.1016/j.ijmedinf.2018.02.010. Epub 2018 Feb 25.

A computer vision-based system for monitoring Vojta therapy.

Author information

1
Research Group of Pattern Recognition, University of Siegen, Germany; College of Information Technology, University of the Punjab, Pakistan. Electronic address: hassan.khan@uni-siegen.de.
2
Research Group of Pattern Recognition, University of Siegen, Germany.
3
College of Information Technology, University of the Punjab, Pakistan.
4
Research Group of Pattern Recognition, University of Siegen, Germany; Faculty of Informatics and Communication, University of Economics in Katowice, Poland.

Abstract

A neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body movement. Consequently, they introduce some diseases in the human e.g. cerebral palsy, spinal scoliosis, peripheral paralysis of arms/legs, hip joint dysplasia and various myopathies. Vojta therapy is considered a useful technique to treat the motor disabilities. In Vojta therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements which the patient is unable to perform in a normal manner. The repetition of stimulation ultimately brings forth the previously blocked connections between the spinal cord and the brain. After few therapy sessions, the patient can perform these movements without external stimulation. In this paper, we propose a computer vision-based system to monitor the correct movements of the patient during the therapy treatment using the RGBD data. The proposed framework works in three steps. In the first step, patient's body is automatically detected and segmented and two novel techniques are proposed for this purpose. In the second step, a multi-dimensional feature vector is computed to define various movements of patient's body during the therapy. In the final step, a multi-class support vector machine is used to classify these movements. The experimental evaluation carried out on the large captured dataset shows that the proposed system is highly useful in monitoring the patient's body movements during Vojta therapy.

KEYWORDS:

Cerebral palsy; Computer vision; Microsoft Kinect; Musculoskeletal system; Spinal scoliosis; Vojta therapy

PMID:
29602437
DOI:
10.1016/j.ijmedinf.2018.02.010
[Indexed for MEDLINE]

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