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Sensors 2016, 16(12), 2151; doi:10.3390/s16122151

Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases

1
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10 box 2446, 3001 Leuven, Belgium
2
iMinds, Medical IT, 3001 Leuven, Belgium
3
University Hospitals Leuven, Division of Rheumatology, Herestraat 49 box 7003, 3000 Leuven, Belgium
4
KU Leuven, Department of Development and Regeneration, Skeletal Biology and Engineering Research Center, Herestraat 49 box 7003, 3000 Leuven, Belgium
5
KU Leuven, Department of Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Unit, Tervuursevest 101 box 1501, 3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 31 October 2016 / Revised: 5 December 2016 / Accepted: 12 December 2016 / Published: 16 December 2016
(This article belongs to the Special Issue Wearable Biomedical Sensors)
View Full-Text   |   Download PDF [2769 KB, uploaded 16 December 2016]   |  

Abstract

One of the important aspects to be considered in rheumatic and musculoskeletal diseases is the patient’s activity capacity (or performance), defined as the ability to perform a task. Currently, it is assessed by physicians or health professionals mainly by means of a patient-reported questionnaire, sometimes combined with the therapist’s judgment on performance-based tasks. This work introduces an approach to assess the activity capacity at home in a more objective, yet interpretable way. It offers a pilot study on 28 patients suffering from axial spondyloarthritis (axSpA) to demonstrate its efficacy. Firstly, a protocol is introduced to recognize a limited set of six transition activities in the home environment using a single accelerometer. To this end, a hierarchical classifier with the rejection of non-informative activity segments has been developed drawing on both direct pattern recognition and statistical signal features. Secondly, the recognized activities should be assessed, similarly to the scoring performed by patients themselves. This is achieved through the interval coded scoring (ICS) system, a novel method to extract an interpretable scoring system from data. The activity recognition reaches an average accuracy of 93.5%; assessment is currently 64.3% accurate. These results indicate the potential of the approach; a next step should be its validation in a larger patient study. View Full-Text
Keywords: accelerometry; activity capacity; activity performance; activity recognition; interpretable medical scoring systems; physical activity; physical therapy; monitoring accelerometry; activity capacity; activity performance; activity recognition; interpretable medical scoring systems; physical activity; physical therapy; monitoring
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MDPI and ACS Style

Billiet, L.; Swinnen, T.W.; Westhovens, R.; de Vlam, K.; Van Huffel, S. Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases. Sensors 2016, 16, 2151.

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