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• W2995867727 abstract "Physical activity (PA) is a key component in the treatment of a range of chronic health conditions. It is therefore important for researchers and clinicians to accurately assess and monitor PA. Although advances in wearable technology have improved this, there is a need to investigate PA in greater depth than the sum of its total parts. Specifically, linking deep PA data to patient outcomes offers a valuable, and unexplored use for wearable devices. As a result, this paper extracts useful features from accelerometer data (Actigraph GT3X Link), and applies machine learning algorithms to predict daily pain and stiffness. This was applied to a population of 30 arthritis patients and 15 healthy volunteers. Participants were provided with an Actigraph and asked to wear it continuously for 28 days. Results demonstrate that it is possible to predict both pain and stiffness of patients using the extracted accelerometer features." @default.
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• W2995867727 date "2019-01-01" @default.
• W2995867727 modified "2023-09-27" @default.
• W2995867727 title "Are You in Pain? Predicting Pain and Stiffness from Wearable Sensor Activity Data" @default.
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• W2995867727 doi "https://doi.org/10.1007/978-3-030-34885-4_15" @default.
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