%0 Journal Article
%T Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach
%A Kurt De Vlam
%A Lieven Billiet
%A Rene Westhovens
%A Sabine Van Huffel
%A Thijs Swinnen
%J -
%D 2018
%R https://doi.org/10.3390/informatics5020020
%X Abstract 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-Tex
%K physical therapy
%K activity recognition
%K accelerometry
%K tensor decomposition
%K classification with rejection
%U https://www.mdpi.com/2227-9709/5/2/20