Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach
AbstractIn 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
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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.
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.Chicago/Turabian Style
Billiet, Lieven; Swinnen, Thijs; De Vlam, Kurt; Westhovens, Rene; Van Huffel, Sabine. 2018. "Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach." Informatics 5, no. 2: 20.
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