Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
Open accessAbstract
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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Wahid et al. (2015) reported a classification accuracy of 92.6% for Parkinson's disease using random forests and multiple regression normalization on gait data from cameras.
“classification accuracy of 92.6% using random forests and multiple regression normalization”
From Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures (2016) · cites this paper as [43]
The Analysis Results section (H1; page not provided) says Cook and colleagues used random forests to classify healthy older adults and participants with Parkinson disease. It does not report 92.6% accuracy, multiple-regression normalization, or camera-based gait data; the study instead describes smart-home and wearable sensors. The attribution to Wahid et al. also does not match this paper’s authors. (LLM, conf=0.86)
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Research works printed but not linked: 1
- [48]Dawadi P Cook DJ Schmitter-Edgecombe M Longitudinal functional assessment of older adults using smart home sensor data IEEE J Biomed Heal Informatics 2015
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Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review
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- Authors
- Cook, D. J., Schmitter-Edgecombe, M., & Dawadi, P.
- Affiliations
- Washington State University (US)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2015-11
- Volume
- 19
- Issue
- 6
- Pages
- 1882-1892
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- PMC
- PMC4667369
- OpenAlex
- W1936527623
- Cited by
- 123
- References
- 52
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 12 · 2023: 9 · 2022: 10 · 2021: 14 · 2020: 13 · 2019: 12 · 2018: 24 · 2017: 12 · 2016: 8 · 2015: 1
- FWCI
- 13.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1064628)
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, older adults, activities of daily living, home automation, wearable sensors, motion analysis, automatic disease detection, machine learning classification
- MeSH terms
- Machine Learning, Aged, Female, Humans, Human Activities, Male, Parkinson Disease, Monitoring, Ambulatory, Remote Sensing Technology
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