Detection of Near Falls Using Wearable Devices: A Systematic Review

Background and Purpose: Falls among older people are a serious health issue. Remote detection of near falls may provide a new way to identify older people at high risk of falling. This could enable exercise and fall prevention programs to target the types of near falls experienced and the situations that cause near falls before fall-related injuries occur. The purpose of this systematic review was to summarize and critically examine the evidence regarding the detection of near falls (slips, trips, stumbles, missteps, incorrect weight transfer, or temporary loss of balance) using wearable devices. Methods: CINAHL, EMBASE, MEDLINE, Compendex, and Inspec were searched to obtain studies that used a wearable device to detect near falls in young and older people with or without a chronic disease and were published in English. Results: Nine studies met the final inclusion criteria. Wearable sensors used included accelerometers, gyroscopes, and insole force inducers. The waist was the most common location to place a single device. Both high sensitivity (>= 85.7%) and specificity (>= 90.0%) were reported for near-fall detection during various clinical simulations and improved when multiple devices were worn. Several methodological issues that increased the risk of bias were revealed. Most studies analyzed a single or few near-fall types by younger adults in controlled laboratory environments and did not attempt to distinguish naturally occurring near falls from actual falls or other activities of daily living in older people. Conclusions: The use of a single lightweight sensor to distinguish between different types of near falls, actual falls, and activities of daily living is a promising low-cost technology and clinical tool for long-term continuous monitoring of older people and clinical populations at risk of falls. However, currently the evidence is limited because studies have largely involved simulated laboratory events in young adults. Future studies should focus on validating near-fall detection in larger cohorts and include data from (i) people at high risk of falling, (ii) activities of daily living, (iii) both near falls and actual falls, and (iv) naturally occurring near falls.