Abstract
The unintentional injuries due to falls in elderly people give rise to a multitude of health and economic problems due to the growing aging population. The use of early pre-impact fall alarm and self-protective control could greatly reduce fall injuries. This paper aimed to explore and implement a pre-impact fall recognition/alarm method for free-direction fall activities based on understanding of the pre-impact lead time of falls and the angle of body postural stability using an inertial body sensor network. Eight healthy Asian adult subjects were arranged to perform three kinds of daily living activities and three kinds of fall activities. Nine MTx sensor modules were used to measure the body segmental kinematic characteristics of each subject for pre-impact fall recognition/alarm. Our analysis of the kinematic features of human body segments showed that the chest was the optimal sensor placement for an early pre-impact recognition/alarm (i.e., prediction/alarm of a fall event before it happens) and post-fall detection (i.e., detection of a fall event after it already happened). Furthermore, by comparative analysis of threshold levels for acceleration and angular rate, two acceleration thresholds were determined for early pre-impact alarm (7 m/s/s) and post-fall detection (20 m/s/s) under experimental conditions. The critical angles of postural stability of torso segment in three kinds of fall activities (forward, sideway and backward fall) were determined as 23.9 ± 3.3, 49.9 ± 4.1 and 9.9 ± 2.5 degrees, respectively, and the relative average pre-impact lead times were 329 ± 21, 265 ± 35 and 257 ± 36 ms. The results implied that among the three fall activities the sideway fall was associated with the largest postural stability angle and the forward fall was associated with the longest time to adjust body angle to avoid the fall; the backward fall was the most difficult to avoid among the three kinds of fall events due to the toughest combination of shortest lead time and smallest angle of postural stability which made it difficult for the self-protective control mechanism to adjust the body in time to avoid falling down.
Highlights
Falls are the second leading cause of accidental injury deaths worldwide, and annually an estimated424,000 individuals die globally from falls, of which over 80% occur in low- and middle-income countries [1]
We presented a pre-impact fall recognition/alarm method that uses comparative and optimal approaches; it is based on an inertial body sensor network which consists of multiple nodes located on different segments of the human body
The results showed that the chest was the optimal sensor placement for early pre-impact fall recognition/alarm, and it was better to use the acceleration as input parameter for early fall recognition/alarm rather than the angular rate
Summary
Falls are the second leading cause of accidental injury deaths worldwide, and annually an estimated424,000 individuals die globally from falls, of which over 80% occur in low- and middle-income countries [1]. One of the most critical challenges faced by healthcare for the elderly is how to achieve early fall recognition/alarm to prevent falls and maintain safe standing and walking [4,5]. Many researchers have developed a series of methods for early pre-impact fall recognition/alarm. The methods included three main techniques: video-based sensing, ambient sensing and wearable sensing. The video-based technique is widely applied in healthcare; its drawbacks are the high cost as well as some fears regarding the privacy of patients [6]. By using the two methods above it is still very difficult to achieve early pre-impact fall recognition/alarm. The wearable sensing technique appears to be the most convenient for use in biomechanics of fall due to its low-cost, ease-of-use and technical advantages [8,9]
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