Abstract
Low back pain (LBP) is the most significant contributor to years lived with disability in Europe and results in significant financial cost to European economies. Guidelines for the management of LBP have self-management at their cornerstone, where patients are advised against bed rest, and to remain active. In this paper, we introduce SELFBACK, a decision support system used by the patients themselves to improve and reinforce self-management of LBP. SELFBACK uses activity recognition from wearable sensors in order to automatically determine the type and level of activity of a user. This is used by the system to automatically determine how well users adhere to prescribed physical activity guidelines. Important parameters of an activity recognition system include windowing, feature extraction and classification. The choices of these parameters for the SELFBACK system are supported by empirical comparative analyses which are presented in this paper. In addition, two approaches are presented for detecting step counts for ambulation activities (e.g. walking and running) which help to determine activity intensity. Evaluation shows the SELFBACK system is able to distinguish between five common daily activities with 0.9 macro-averaged F1 and detect step counts with 6.4 and 5.6 root mean squared error for walking and running respectively.
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Notes
- 1.
The SELFBACK project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689043.
- 2.
Code and data associated with this paper are accessible from https://github.com/selfback/activity-recognition.
- 3.
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Sani, S., Wiratunga, N., Massie, S., Cooper, K. (2016). SELFBACK—Activity Recognition for Self-management of Low Back Pain. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_21
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DOI: https://doi.org/10.1007/978-3-319-47175-4_21
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