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

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.

Patient experiences of virtual care across specialist neuroscience and psychiatry clinics related to the second wave of the COVID-19 pandemic in Calgary, Alberta

“…Patients indicated that the remote monitoring system was easy to use and improved quality of life. Greene et al [ 49 ] used a smartphone app and machine learning to assess fall risk in patients, finding that fall assessments provided by the app were correlated with patients’ self-reported fall history. Oagaz [ 50 ] developed and tested a virtual reality system that was able to perform eye tracking and motion analysis, suggesting it may be useful in diagnosing patients with neurocognitive disorders.…”

The Developments and Iterations of a Mobile Technology-Based Fall Risk Health Application

“…Compared to other fall risk apps, Steady, Steady-MS, and Steady-Wheels are some of the few apps that have been designed for specific clinical populations and evaluated for their validity, reliability, and usability. Greene et al ( 26 ) used machine learning methods to develop an algorithm for predicting falls using mobile technology but did not test their app with older adult users. Mansson et al ( 27 , 28 ) developed an app to measure leg strength and balance and tested its usability with older adults but did not measure other fall risk factors.…”

Telemedicine in neurology: advances and possibilities

“…Smartphones can capture voice and tremor and, and through machine learning, we can monitor or even diagnose Parkinson' s disease [41][42][43] . Assessing balance and risk of falls using a validated smartphone app can increase safety and reduce the number of falls in older adults 44 .…”