A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration

Chao, Pei-Kuang; Chan, Hsiao‐Lung; Tang, Feng‐Yao; Chen, Yu-Chuan; Wong, May‐Kuen

doi:10.1088/0967-3334/30/10/004

Cited by 39 publications

(25 citation statements)

References 14 publications

(30 reference statements)

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“…Bourke et al [20] monitored the posture change before and after a fall, without the criteria of a lying end posture. In some fall detection algorithms, the horizontal end posture for the lying posture after the fall-associated impact has been used as one criterion for a fall [26,35]. …”

Section: Discussionmentioning

confidence: 99%

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

Korpelainen²,

et al. 2014

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Background: About a third of home-dwelling older people fall each year, and institutionalized older people even report a two- or threefold higher rate for falling. Automatic fall detection systems have been developed to support the independent and secure living of the elderly. Even though good fall detection sensitivity and specificity in laboratory settings have been reported, knowledge about the sensitivity and specificity of these systems in real-life conditions is still lacking. Objective: The aim of this study was to evaluate the long-term fall detection sensitivity and false alarm rate of a fall detection prototype in real-life use. Methods: A total of 15,500 h of real-life data from 16 older people, including both fallers and nonfallers, were monitored using an accelerometry-based sensor system with an implemented fall detection algorithm. Results: The fall detection system detected 12 out of 15 real-life falls, having a sensitivity of 80.0%, with a false alarm rate of 0.049 alarms per usage hour with the implemented real-time system. With minor modification of data analysis the false alarm rate was reduced to 0.025 false alarms per hour, equating to 1 false fall alarm per 40 usage hours. Conclusion: These data suggest that automatic accelerometric fall detection systems might offer a tool for improving safety among older people.

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Section: Discussionmentioning

confidence: 99%

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

Korpelainen²,

et al. 2014

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“…Furthermore, the common assumption that a horizontal trunk position classifies a resting phase was not confirmed by the analysed data on trunk pitch angle. This potentially false assumption could hamper fall detection algorithms from detecting specific post-impact lying positions [15,16] . Fallers with long lying periods commonly attempted multiple times to stand up over their entire "resting" period.…”

Section: Discussionmentioning

confidence: 99%

Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls

et al. 2017

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Background: Lying on the floor for a long time after falls, regardless of whether an injury results, remains an unsolved health care problem. In order to develop efficient and acceptable fall detection and reaction approaches, it is relevant to improve the understanding of the circumstances and the characteristics of post-impact responses and the return or failure to return to pre-fall activities. Falls are seldom observed by others; until now, the knowledge about movement kinematics during falls and following impact have been anecdotal. Objective: This study aimed to analyse characteristics of the on-ground and recovery phases after real-world falls. The aim was to compare self-recovered falls (defined as returns to standing from the floor) and non-recovered falls with long lies. Methods and Participants: Data from subjects in different settings and of different populations with high fall risk were included. Real-world falls collected by inertial sensors worn on the lower back were taken from the FARSEEING database if reliable information was available from fall reports and sensor signals. Trunk pitch angle and acceleration were analysed to describe different patterns of recovery movements while standing up from the floor after the impact of a fall. Results: Falls with successful recovery, where an upright posture was regained, were different from non-recovered falls in terms of resting duration (median 10.5 vs. 34.5 s, p = 0.045). A resting duration longer than 24.5 s (area under the curve = 0.796) after the fall impact was a predictor for the inability to recover to standing. Successful recovery to standing showed lower cumulative angular pitch movement than attempted recovery in fallers that did not return to a standing position (median = 76°, interquartile range 24-170° vs. median = 308°, interquartile range 30-1,209°, p = 0.06). Conclusion: Fall signals with and without successful returns to standing showed different patterns during the phase on the ground. Characteristics of real-world falls provided through inertial sensors are relevant to improve the classification and the sensing of falls. The findings are also important for redesigning emergency response processes after falls in order to better support individuals in case of an unrecovered fall. This is crucial for preventing long lies and other fall-related incidents that require an automated fall alarm.

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“…The first class involves onbody motion sensing, where falling is inferred by characteristic patterns in body acceleration and motion [6,[41][42][43][44][45][46], as illustrated in Fig. 2 …”

Section: Fall Detectionmentioning

confidence: 99%

“…This is to demonstrate that a Parkinson's disease limb tremor monitoring system, such as in [2], could be easily upgraded with fall-detection capabilities, without additional W 2 SNs or moving nodes around. For a pure fall-detection WBAN disregarding primary application sensor placement, it is worth noting that studies on sensor placement to record acceleration due to falls has so far been inconclusive [46]; some have suggested behind the ear [41], the trunk [6] or the head and waist [43] areas as being the optimum locations for a fall sensor.…”

Section: Fall Detection Enhancement Experimental Approachmentioning

confidence: 99%

Wireless Body Area Network Node Localization Using Small-Scale Spatial Information

González-Valenzuela

Leung

2013

IEEE J. Biomed. Health Inform.

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Deploying wireless body area networks (WBANs) in the long-term at-home monitoring of a patient's physiological and bio-kinetic conditions has become increasingly prevalent. However, such WBANs do not typically incorporate mechanisms to detect and correct for the possibility of accidentally switching up wearable wireless sensor nodes (W 2 SNs), where a node assigned to one limb is placed on another, and vice-versa, leading to possible incorrect prognoses from interpreting the data.In this thesis, we present a new scheme to automatically identify and To quantify and validate the accuracy, consistency and reliability of this localization scheme, a statistical analysis on a set of commercially-available air pressure sensors and an experimental prototype WBAN is conducted to examine the scheme's performance and limitations. This study has verified that this approach is indeed capable of distinguishing between positions indicative of expected separation between different limbs of the patient's body.Based on a 60cm separation between nodes, the statistical analysis consistently exceeded 95% accuracy within the confidence interval (CI), demonstrating great promise for incorporation into commercial WBANs.We also present and experimentally demonstrate an enhancement aiming

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A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration

Cited by 39 publications

References 14 publications

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls

Wireless Body Area Network Node Localization Using Small-Scale Spatial Information

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