Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare
State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarse-grained activities (sitting, standing, walking, or lying down), but can't distinguish complex activities (sitting on the floor versus the sofa or bed). Such schemes often aren't effective for emerging critical healthcare applications - for example, in remote monitoring of patients with Alzheimer's disease, bulimia, or anorexia - because they require a more comprehensive, contextual, and fine-grained recognition of complex daily user activities. Here, a novel approach for in-home, fine-grained activity recognition uses multimodal wearable sensors on multiple body positions, along with lightly deployed Bluetooth beacons in the environment.
D. De et al., "Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare," IEEE Internet Computing, vol. 19, no. 5, pp. 26-35, Institute of Electrical and Electronics Engineers (IEEE), Sep 2015.
The definitive version is available at https://doi.org/10.1109/MIC.2015.72
Keywords and Phrases
Artificial intelligence; Bluetooth; Health care; Multi agent systems; Neurodegenerative diseases; Pattern recognition; Random processes; Remote patient monitoring; Wearable technology; Activity recognition; Alzheimer's disease; Ambient environment; Conditional random field; Health care application; Supervised classifiers; Wearable devices; Wearables; Wearable sensors; Internet/Web technologies; Multiagent systems; Smart healthcare
International Standard Serial Number (ISSN)
Article - Journal
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