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Unsupervised assessment of balance and falls risk using a smartphone and machine learning

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Show simple item record Greene, Barry R. McManus, Killian Ader, Lilian Genaro Motti Caulfield, Brian 2021-07-30T08:25:48Z 2021-07-30T08:25:48Z 2021
dc.description peer-reviewed en_US
dc.description.abstract 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. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Sensors;21, 4770
dc.subject falls en_US
dc.subject balance en_US
dc.subject postural sway en_US
dc.title Unsupervised assessment of balance and falls risk using a smartphone and machine learning en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.3390/s21144770
dc.contributor.sponsor IRC en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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