Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors

Becker, Clemens; Schwickert, Lars; Mellone, Sabato; Bagalà, Fabio; Chiari, Lorenzo; Helbostad, Jorunn L.; Zijlstra, Wiebren; Aminian, Kamiar; Bourke, Alan K.; Todd, Chris; Bandinelli, Stefania; Kerse, Ngaire; Klenk, Jochen

doi:10.1007/s00391-012-0403-6

Cited by 51 publications

(37 citation statements)

References 29 publications

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“…Therefore, it is widely used to develop wearable devices which allow the measurement of physical activity under real-life environment. This includes indoor and outdoor activities as well as recordings in very private areas like the bathroom or the toilet [10]. Meanwhile, smart phone integrated with inertia sensors is more and more popular; many works have been 2 International Journal of Distributed Sensor Networks done for the fall detection on the smartphone.…”

Section: Introductionmentioning

confidence: 99%

A Smart Device Enabled System for Autonomous Fall Detection and Alert

Jian

Wang

2016

International Journal of Distributed Sensor Networks

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The activity model based on 3D acceleration and gyroscope is created in this paper, and the difference between the activities of daily living (ADLs) and falls is analyzed at first. Meanwhile, the NN algorithm and sliding window are introduced to develop a smart device enabled system for fall detection and alert, which is composed of a wearable motion sensor board and a smart phone. The motion sensor board integrated with triaxial accelerometer, gyroscope, and Bluetooth is attached to a custom vest worn by the elderly to capture the reluctant acceleration and angular velocity of ADLs in real time. The stream data via Bluetooth is then sent to a smart phone, which runs a program based on the NN algorithm and sliding window to analyze the stream data and detect falls in the background. At last, the experiment shows that the system identifies simulated falls from ADLs with a high accuracy of 97.7%, while sensitivity and specificity are 94% and 99%, respectively. Besides, the smart phone can issue an alarm and notify caregivers to provide timely and accurate help for the elderly, as soon as a fall is detected.

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Section: Introductionmentioning

confidence: 99%

A Smart Device Enabled System for Autonomous Fall Detection and Alert

Jian

Wang

2016

International Journal of Distributed Sensor Networks

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“…posture angle, RSS impact profile shape and the detection of the different phases of the falls [11], the detection of real-world falls could become more robust if these are combined along with certain machine learning techniques.…”

Section: Resultsmentioning

confidence: 99%

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population

Bourke

Klenk

Schwickert

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However, <;7% of studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events. We have extracted temporal and kinematic parameters to further improve the development of fall detection algorithms. A total of 100 real-world falls were analysed. Subjects with a known fall history were recruited, inertial sensors were attached to L5 and a fall report, following a fall, was used to extract the fall signal. This data-set was examined, and variables were extracted that include upper and lower impact peak values, posture angle change during the fall and time of occurrence. These extracted parameters, can be used to inform the design of fall-detection algorithms for real-world falls detection in the elderly.

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“…These falls were further divided into three categories: (1) self-recovered falls, containing patterns of regaining a standing position following an initial resting phase of at least 1 s, or (2) a direct recovery pattern with the faller initiating standing up within 1 s after the impact without resting, or (3) non-recovered falls, where the fallers were not able to stand up on their own and needed help from another person to stand up as confirmed by the report. The marking was based on a consented fall phase model [13] . In order to estimate trunk pitch angle during resting and recovery, quaternions were calculated using a sensor fusion algorithm [14] .…”

Section: Methodsmentioning

confidence: 99%

“…To further analyse this problem, real-world falls data are needed. In a recently EC-funded project (FARSEEING, www.farseeingresearch.eu), we were able to collect more than 200 realworld falls [12,13] . To the best of our knowledge, there are currently no other publications including post-impact information from real-world falls.…”

Section: Introductionmentioning

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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Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors

Cited by 51 publications

References 29 publications

A Smart Device Enabled System for Autonomous Fall Detection and Alert

A Smart Device Enabled System for Autonomous Fall Detection and Alert

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population

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

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