Cited by 123Context-Aware Activity Recognition SystemsPhysical Activity and HealthBalance, Gait, and Falls Prevention

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

Open access
Cook, Diane J., Schmitter-Edgecombe, Maureen, Dawadi, Prafulla·2015·IEEE Journal of Biomedical and Health Informatics

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/jbhi.2015.2461659

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  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

Cited by

Sorted by citations

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
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review