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

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 anal...

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Published in:IEEE journal of biomedical and health informatics Vol. 19; no. 6; pp. 1882 - 1892
Main Authors: Cook, Diane J., Schmitter-Edgecombe, Maureen, Dawadi, Prafulla
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adults
Aged
Female
Health
Human Activities - classification
Humans
Impact analysis
Machine Learning
Male
mild cognitive impairment (MCI)
Monitoring, Ambulatory - methods
Old people
Parkinson disease
Parkinson disease (PD)
Parkinson Disease - physiopathology
Parkinson's disease
Patient monitoring
Pattern recognition
Pervasive computing
Remote Sensing Technology
Smart buildings
Smart homes
Wearable sensors
mild cognitive impairment (MCI)
Parkinson disease (PD)
pervasive computing
Machine learning
ISSN:2168-2194, 2168-2208
Online Access:Get full text
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Abstract 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.
AbstractList 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.
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 ( [Formula Omitted]) 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.
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 AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.
Author Dawadi, Prafulla
Cook, Diane J.
Schmitter-Edgecombe, Maureen
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Keywords mild cognitive impairment (MCI)
Parkinson disease (PD)
pervasive computing
Machine learning
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Snippet 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...
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SubjectTerms Adults
Aged
Female
Health
Human Activities - classification
Humans
Impact analysis
Machine Learning
Male
mild cognitive impairment (MCI)
Monitoring, Ambulatory - methods
Old people
Parkinson disease
Parkinson disease (PD)
Parkinson Disease - physiopathology
Parkinson's disease
Patient monitoring
Pattern recognition
Pervasive computing
Remote Sensing Technology
Smart buildings
Smart homes
Wearable sensors
Title Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
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