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Activity Recognition for Physical Therapy Fusing Signal Processing Features and Movement Patterns

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

This paper discusses the classification of activities in the context of physical therapy. Usually, specific standardized activities are subjectively assessed, often by means of a patient-reported questionnaire, to estimate a patient’s activity capacity, defined as the ability to execute a task. Automatic recognition of these activities is of vital importance for a more objective and quantitative approach to the problem. The proposed accelerometry-based algorithm merges standard signal processing features with information obtained from direct activity pattern matching using dynamic time warping (DTW) in a linear model. This study with 28 spondyloarthritis patients performing 10 activities shows the improvement in activity classification accuracy due to the fusion of the two approaches, up to 93.6%. This is a significant increase compared to similar models based on either of the approaches alone (p < 0.01). Although this paper mainly contributes to the activity recognition step, it also briefly discusses the advantages of the approach with regard to further automated evaluation of the recognized activities.
Boek: iWOAR '16: Proceedings of the 3rd International Workshop on Sensor-based Activity Recognition and Interaction
Aantal pagina's: 6
Jaar van publicatie:2016