Next Article in Journal
Electromagnetic–Acoustic Sensing for Biomedical Applications
Previous Article in Journal
Sensoring a Generative System to Create User-Controlled Melodies
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(10), 3202;

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

Research Group for Pattern Recognition, University of Siegen, 57076 Siegen, Germany
College of Information Technology, University of the Punjab, 54000 Lahore, Pakistan
Author to whom correspondence should be addressed.
Received: 3 August 2018 / Revised: 17 September 2018 / Accepted: 20 September 2018 / Published: 21 September 2018
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [2683 KB, uploaded 22 September 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top