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Article

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

1
Research Group for Pattern Recognition, University of Siegen, 57076 Siegen, Germany
2
College of Information Technology, University of the Punjab, 54000 Lahore, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3202; https://doi.org/10.3390/s18103202
Received: 3 August 2018 / Revised: 17 September 2018 / Accepted: 20 September 2018 / Published: 21 September 2018
(This article belongs to the Section Physical Sensors)
Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness. View Full-Text
Keywords: movement analysis; infantile movement disorders; part-based model; k-means clustering movement analysis; infantile movement disorders; part-based model; k-means clustering
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MDPI and ACS Style

Khan, M.H.; Schneider, M.; Farid, M.S.; Grzegorzek, M. Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model. Sensors 2018, 18, 3202. https://doi.org/10.3390/s18103202

AMA Style

Khan MH, Schneider M, Farid MS, Grzegorzek M. Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model. Sensors. 2018; 18(10):3202. https://doi.org/10.3390/s18103202

Chicago/Turabian Style

Khan, Muhammad Hassan, Manuel Schneider, Muhammad Shahid Farid, and Marcin Grzegorzek. 2018. "Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model" Sensors 18, no. 10: 3202. https://doi.org/10.3390/s18103202

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