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Informatics 2018, 5(2), 20;

Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach

STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10 box 2446, B-3001 Leuven, Belgium
IMEC Leuven, B-3001 Leuven, Belgium
Division of Rheumatology, University Hospitals Leuven, Herestraat 49 box 7003, B-3000 Leuven, Belgium
Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, B-3000 Leuven, Belgium
Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101 box 1501, B-3001 Leuven, Belgium
Authors to whom correspondence should be addressed.
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 9 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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In current clinical practice, functional limitations due to chronic musculoskeletal diseases are still being assessed subjectively, e.g., using questionnaires and function scores. Performance-based methods, on the other hand, offer objective insights. Hence, they recently attracted more interest as an additional source of information. This work offers a step towards the shift to performance-based methods by recognizing standardized activities from continuous readings using a single accelerometer mounted on a patient’s arm. The proposed procedure consists of two steps. Firstly, activities are segmented, including rejection of non-informative segments. Secondly, the segments are associated to predefined activities using a multiway pattern matching approach based on higher order discriminant analysis (HODA). The two steps are combined into a multi-layered framework. Experiments on data recorded from 39 patients with spondyloarthritis show results with a classification accuracy of 94.34% when perfect segmentation is assumed. Automatic segmentation has 89.32% overlap with this ideal scenario. However, combining both drops performance to 62.34% due to several badly-recognized subjects. Still, these results are shown to significantly outperform a more traditional pattern matching approach. Overall, the work indicates promising viability of the technique to automate recognition and, through future work, assessment, of functional capacity. View Full-Text
Keywords: physical therapy; activity recognition; accelerometry; tensor decomposition; classification with rejection physical therapy; activity recognition; accelerometry; tensor decomposition; classification with rejection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Billiet, L.; Swinnen, T.; De Vlam, K.; Westhovens, R.; Van Huffel, S. Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach. Informatics 2018, 5, 20.

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