Promises and Challenges in the Use of Wearable Sensors and Nonlinear Signal Analysis for Balance and Fall Risk Assessment in Older Adults

Abstract

The rise of wearable technologies is enabling novel ways of assessing balance and risk of falling in later life. Wearable inertial sensors are a promising addition to clinical balance assessment tools since they provide an objective and accurate fall risk assessment. Moreover, wearable devices also enable the ambulatory monitoring of physiological and behavioural variables, which can be used to infer health status and health-related behaviours linked to impaired balance and fall risk (e.g. sleep disturbances and poor sleep quality). This situation could potentially expand the prevailing paradigm in fall prevention, from the current one mainly involving the occasional assessment of risk factors to a new paradigm also including the continuous monitoring and detection of short-lived factors that might result in an imminent fall. Additionally, the diffusion of the dynamical systems theory and methods within the medical research community is inspiring a new approach to the study of ageing and balance in older adults. In particular, nonlinear signal analysis methods could potentially provide with further information on the underlying control mechanisms in ageing and produce more sensitive measures of fall risk. However, there are several challenges in the adoption of these devices and methods, which still preclude a firm conclusion on their clinical value. This paper summarises three studies performed to address some of these challenges and distils the lessons learnt from them. Collectively, the findings of this research confirm that these sensors and methods could improve currents tools and practices for balance and fall risk assessment, and provides some insights concerning their optimal use.