Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques

Diane J Cook; Maureen Schmitter-Edgecombe; Prafulla Dawadi

doi:10.1109/JBHI.2015.2461659

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Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques

Journal article

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Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques

Diane J Cook, Maureen Schmitter-Edgecombe and Prafulla Dawadi

IEEE journal of biomedical and health informatics, Vol.19(6), pp.1882-1892

11/2015

DOI: https://doi.org/10.1109/JBHI.2015.2461659

Handle:

https://hdl.handle.net/2376/108687

PMCID: PMC4667369

PMID: 26259225

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Abstract

Pervasive computing

Patient monitoring

Parkinson disease (PD)

Parkinson's disease

Smart homes

Machine learning

mild cognitive impairment (MCI)

Wearable sensors

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while (n = 84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an area under the ROC curve value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.

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Title: Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
Creators: Diane J Cook - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
Maureen Schmitter-Edgecombe - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
Prafulla Dawadi - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
Publication Details: IEEE journal of biomedical and health informatics, Vol.19(6), pp.1882-1892
Academic Unit: Psychology, Department of; Electrical Engineering and Computer Science, School of
Publisher: IEEE
Grant note: R01EB009675 / National Institute of Biomedical Imaging and Bioengineering (10.13039/100000070) 1064628 / National Science Foundation (10.13039/100000001)
Identifiers: 99900547003501842
Language: English
Resource Type: Journal article