Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities

Bourke, Alan K.; Ven, Pepijn van de; Gamble, Mary; O'Connor, Raymond G.; Murphy, Kieran J.; Bogan, E.; McQuade, Eamonn; Finucane, Paul; ÓLaighin, Gearóid; Nelson, John

doi:10.1016/j.jbiomech.2010.07.005

Cited by 192 publications

(127 citation statements)

References 15 publications

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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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“…The pre impact phase has been used for fall detection algorithms [11,16,17,19,20] and as an indicator for triggering the inflation of a wearable airbag to protect thighs from high fall-associated impacts [21]. The pre impact phase has been characterized from experimental falls by a body tilt angle [22], the start of the fall based on the acceleration sum vector (SV TOT ) signal [17,23], or velocity toward the ground.…”

Section: Discussionmentioning

confidence: 99%

“…Bourke et al [11,12] tested a fall detection systems among older people in their home environment. However, no accidental falls occurred during the test periods.…”

Section: Introductionmentioning

confidence: 99%

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

Kangas¹,

Vikman²,

Nyberg³

et al. 2012

Gait & Posture

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“…We discovered that knee sensor is most responsive to the fall action using angular acceleration detections. This is a new discovery different from other Zigbee fall sensor studies [12].…”

Section: Resultsmentioning

confidence: 80%

A Multimodal Sensing System for Elder Fall Detection

Shih

2015

Computer Science and Its Applications

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Abstract. Among the many health care issues for elder people, fall detection has shown up as primary focus. We propose a sensor fusion concept for elder fall monitoring and analysis. The idea is to integrate depth sensor and motion sensor. We illustrate the sensor fusion concept and implement the wireless fall detection sensor sub-system. We experiment with angular acceleration data in three limbs parts of human test target. Different life patterns of elderly are selected to test the motion monitoring system: walking and falling. Two methods are employed as diagnosis tool. The first is the energy expenditure method, while the second is the neural network method. The first tests the sensitivity of motion sensor, while the second filters out the noisy motion data for correct classification purpose. We discovered that the trained weights and bios from the neural network based on three limbs parts measurement data has allowed us to differentiate fall from regular walk motion with high accuracy.

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Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities

Cited by 192 publications

References 15 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

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

A Multimodal Sensing System for Elder Fall Detection

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