Novel sensing technology in fall risk assessment in older adults: a systematic review

Abstract

BackgroundFalls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.MethodsA systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).ResultsTwenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.ConclusionsA wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

Fall Risk Prediction via Classification of Lower Extremity Strength in Older Adults with Exergame-Collected Data

“…The possibility of using the Wii Balance Board (WBB) to estimate whether the player is at an increased falling risk has been identified [4]. However, related publications mention the need for additional studies, particularly in finding direct relationships between sensor data and clinically meaningful fall risk estimation methods.…”

The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment

“…Even though such standardized tests are relatively easy to conduct, it is still underutilized and not routinely integrated into clinical practice. Partially due to clinicians' time constraints and competing medical priorities, lack of access to lab-grade advanced testing equipment (such as motion 2 of 12 capture device, force platform), as well as lack of clinical expertise [9], thus limiting access to fall risk screening in the community dwelling older adults. Consequently, older adults remain unaware of their individual fall risk, appropriate fall prevention approaches and at elevated risk of falls.…”

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Behavior modelling and sensing for machinery operations using smartphone’s sensor data: A case study of forage maize sowing

“…Further, using the machine-learning classifiers such as SVM, decision tree, and neural network, can classify and identify different behaviors including stalling, walking, running, and etc., which can achieve a recognition accuracy about 95% [17,18] . When combined with geographic information, the method is widely used in caring for the old and the young, and for sports pattern recognition [19][20][21][22] . In the field of automobile driving, accelerometers, and gyroscopes are widely used in driving style and driving safety recognition [23][24][25] , such as sudden acceleration, sudden braking, speeding, and sharply turning [24,26,27] .…”