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Article

Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning

1
Kinesis Health Technologies, D04 V2N9 Dublin, Ireland
2
Insight Centre, University College Dublin, D04 N2E5 Dublin, Ireland
3
Department Computer Science and Information Systems, University of Limerick, V94 XT66 Limerick, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Isabel De la Torre Díez
Sensors 2021, 21(14), 4770; https://doi.org/10.3390/s21144770
Received: 17 June 2021 / Revised: 8 July 2021 / Accepted: 9 July 2021 / Published: 13 July 2021
(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)
Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk. Assessment of balance and falls risk using a validated smartphone app may lessen the need for clinical assessments which can be expensive, requiring non-portable equipment and specialist expertise. This study reports results for the real-world deployment of a smartphone app for self-directed, unsupervised assessment of balance and falls risk. The app relies on a previously validated algorithm for assessment of balance and falls risk; the outcome measures employed were trained prior to deployment on an independent data set. Results for a sample of 594 smartphone assessments from 147 unique phones show a strong association between self-reported falls history and the falls risk and balance impairment scores produced by the app, suggesting they may be clinically useful outcome measures. In addition, analysis of the quantitative balance features produced seems to suggest that unsupervised, self-directed assessment of balance in the home is feasible. View Full-Text
Keywords: falls; balance; postural sway; smartphone; inertial sensor; accelerometer; gyroscope falls; balance; postural sway; smartphone; inertial sensor; accelerometer; gyroscope
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MDPI and ACS Style

Greene, B.R.; McManus, K.; Ader, L.G.M.; Caulfield, B. Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning. Sensors 2021, 21, 4770. https://doi.org/10.3390/s21144770

AMA Style

Greene BR, McManus K, Ader LGM, Caulfield B. Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning. Sensors. 2021; 21(14):4770. https://doi.org/10.3390/s21144770

Chicago/Turabian Style

Greene, Barry R., Killian McManus, Lilian G.M. Ader, and Brian Caulfield. 2021. "Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning" Sensors 21, no. 14: 4770. https://doi.org/10.3390/s21144770

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