Nonlinear analysis of human physical activity patterns in health and disease.

Détails

ID Serval
serval:BIB_C0871E61F367
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Nonlinear analysis of human physical activity patterns in health and disease.
Périodique
Physical Review. E
Auteur(s)
Paraschiv-Ionescu A., Buchser E., Rutschmann B., Aminian K.
ISSN
1539-3755
Statut éditorial
Publié
Date de publication
2008
Peer-reviewed
Oui
Volume
77
Numéro
2 Pt 1
Pages
021913
Langue
anglais
Résumé
The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.
Mots-clé
Activities of Daily Living, Algorithms, Diagnosis, Computer-Assisted/methods, Humans, Monitoring, Ambulatory/methods, Motor Activity, Movement, Nonlinear Dynamics, Pain/diagnosis, Pain/physiopathology, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity
Pubmed
Web of science
Création de la notice
13/10/2009 12:04
Dernière modification de la notice
03/03/2018 21:06
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