Advanced 3D movement analysis algorithms for robust functional capacity assessment

Asma Hassani; Alexandre Kubicki; France Mourey; Fan Yang

doi:10.4338/ACI-2016-11-RA-0199

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2017; 08(02): 454-469
DOI: 10.4338/ACI-2016-11-RA-0199

Research Article

Schattauer GmbH

Advanced 3D movement analysis algorithms for robust functional capacity assessment

Asma Hassani

¹Le2i UMR6306, CNRS, Arts et Métiers, Univ. Bourgogne Franche-Comté, France

,

Alexandre Kubicki

²IFMS Montbeliard, France

,

France Mourey

³Institut National de la Santé et de la Recherche Médicale (INSERM) U1093, Cognition Action et Plasticité Sensori-Motrice, BP 27877, campus universitaire, Université de Bourgogne, Dijon, France

⁴Faculté de Médecine, Université de Bourgogne, Dijon, France

,

Fan Yang

¹Le2i UMR6306, CNRS, Arts et Métiers, Univ. Bourgogne Franche-Comté, France

› Institutsangaben

Abstract

Summary

Objectives: We developed a novel system for in home functional capacities assessment in frail older adults by analyzing the Timed Up and Go movements. This system aims to follow the older people evolution, potentially allowing a forward detection of motor decompensation in order to trigger the implementation of rehabilitation. However, the pre-experimentations conducted on the ground, in different environments, revealed some problems which were related to KinectTM operation. Hence, the aim of this actual study is to develop methods to resolve these problems.

Methods: Using the KinectTM sensor, we analyze the Timed Up and Go test movements by measuring nine spatio-temporal parameters, identified from the literature. We propose a video processing chain to improve the robustness of the analysis of the various test phases: automatic detection of the sitting posture, patient detection and three body joints extraction. We introduce a realistic database and a set of descriptors for sitting posture recognition. In addition, a new method for skin detection is implemented to facilitate the patient extraction and head detection. 94 experiments were conducted to assess the robustness of the sitting posture detection and the three joints extraction regarding condition changes.

Results: The results showed good performance of the proposed video processing chain: the global error of the sitting posture detection was 0.67%. The success rate of the trunk angle calculation was 96.42%. These results show the reliability of the proposed chain, which increases the robustness of the automatic analysis of the Timed Up and Go.

Conclusions: The system shows good measurements reliability and generates a note reflecting the patient functional level that showed a good correlation with 4 clinical tests commonly used. We suggest that it is interesting to use this system to detect impairment of motor planning processes.

Citation: Hassani A, Kubicki A, Mourey F, Yang F. Advanced 3D movement analysis algorithms for robust functional capacity assessment. Appl Clin Inform 2017; 8: 454–469 https://doi.org/10.4338/ACI-2016-11-RA-0199

Keywords

Patient self-care home care and e-health - clinical informatics - sitting posture recognition - skin detection - 3D real-time video processing

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Referenzen

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