A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results

Abstract

Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.

Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks

“…This enables the assistive device to adapt to changes in human gait, allowing smoother synchronisation with user intentions and minimising interruptions when the user changes their movement pattern (Elliott et al, 2014;Zhang et al, 2017;Ding et al, 2018;Zaroug et al, 2019). A known future trajectory might also monitor the risk of balance loss, tripping and falling, in which impending incidents can be remotely reported for early intervention (Begg and Kamruzzaman, 2006;Begg et al, 2007;Nait Aicha et al, 2018;Hemmatpour et al, 2019;Naghavi et al, 2019). Since 60 ms falls in the range of slow (60-120 ms) and fast (10-50 ms) twitch motor units (Winter, 2009), this would enable wearable devices such as IMUs to alert (e.g., by audio/visual signal) an elderly user about an imminent risk of tripping and potentially gives them a chance to adjust their gait accordingly.…”

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

“…The statistical approaches often lead to low classification accuracy and prove to be less efficient with noisy data. Therefore, ML techniques are widely used for fall identification and prevention [ 43 ]. Basic fall activities that are identified are falling forward, falling backward, falling sideways, spinning clockwise, and spinning anticlockwise [ 44 ].…”

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

“…These approaches are based on the integration of machine learning, IoT (Internet of things) devices, imaging techniques [ 12 ], etc. The continuous monitoring of the elderly person using either wearable or non-wearable devices and finding the probability of their fall in advance is known as fall prediction [ 13 ]; however, fall prediction is more concerned with the detection of fall risk factors. It requires a highly accurate prediction mechanism that could respond instantly in no time.…”

“…These devices differ in their location of the mount, response time, size, etc. Some of the devices are listed [ 10 , 11 , 12 , 13 ] below: MobileHelp Medical Guardian LifeFone Bay Alarm Medical GreatCall Lively Mobile Plus Apple Watch …”