Sensors, Vol. 18, Pages 3202: Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Sensors, Vol. 18, Pages 3202: Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model Sensors doi: 10.3390/s18103202 Authors: Muhammad Khan Manuel Schneider Muhammad Farid Marcin Grzegorzek 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 us...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research