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Article

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

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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
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IMEC Leuven, B-3001 Leuven, Belgium
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Division of Rheumatology, University Hospitals Leuven, Herestraat 49 box 7003, B-3000 Leuven, Belgium
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Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Herestraat 49 box 805, B-3000 Leuven, Belgium
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Musculoskeletal Rehabilitation Research Unit, Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101 box 1501, B-3001 Leuven, Belgium
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Authors to whom correspondence should be addressed.
Informatics 2018, 5(2), 20; https://doi.org/10.3390/informatics5020020
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)
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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MDPI and ACS Style

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. https://doi.org/10.3390/informatics5020020

AMA Style

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(2):20. https://doi.org/10.3390/informatics5020020

Chicago/Turabian Style

Billiet, Lieven, Thijs Swinnen, Kurt De Vlam, Rene Westhovens, and Sabine Van Huffel. 2018. "Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach" Informatics 5, no. 2: 20. https://doi.org/10.3390/informatics5020020

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