Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare

Abstract

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.

Department(s)

Computer Science

Comments

The US National Science Foundation supported this work in part through grants 1404673, 1404677, 1254117, and 1205695.

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)

1089-7801; 1941-0131

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2015

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