Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor Network

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.

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

“…Information on whether the participants were patients and/or healthy was provided in 39/60 (65%) of the articles (Table 3). An additional four studies, [9,21,22,38] present the distribution of patients and/or healthy for some of the reported sub-studies. Two groups including 84 participants in total were representing patients and healthy participants in [19].…”

Exploration and Comparison of the Pre-impact Lead Time of Active and Passive Falls Based on Inertial Sensors

“…Furthermore, two acceleration thresholds were determined for early pre-impact alarm (4.5 m/s/s) and post-fall detection (25 m/s/s) in experimental active fall conditions [15]. The relationship between body dip angles and PLT also in experimental active fall conditions was also investigated [3]. The present study focuses on the PLT of active and passive falls.…”

“…2 shows that the two kinds of fall activities were recognized from ADL with 100% sensitivity and 100% specificity with the threshold of 25m/s/s, using the sensor data from the waist, as it is one of the best positions for pre-fall and fall detection. The details could be found in references [3] and [15]. Hence, the subsequent analyses are all based on the waist node.…”

“…Falls in elderly people become a major health problem around the world, not only because of the disabling fractures and psychological illness, but also for their high social and economic costs. One of the most critical challenges faced to the healthcare for the elderly is the realization of early fall recognition and alarm to prevent falls and keep elder lies doing activities of daily life safely [3].…”

Biological information wireless monitoring system

“…For example, in the industry, it is always required to set up an intelligent monitoring system to collect information from workshop. The monitoring system is generated to improve the security of industrial processes by reducing the time of handling dangers [12][13][14][15]; in the medical area, Hoi Ching Tung and Kim Fung Tsang built a physiological and psychological wireless monitoring network for patients by optimizing network architecture, algorithm and ZigBee technology in embedded system [16][17][18][19][20][21].…”