Literature DB >> 28848150

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

Lars Schwickert1, Jochen Klenk, Wiebren Zijlstra, Maxim Forst-Gill, Kim Sczuka, Jorunn L Helbostad, Lorenzo Chiari, Kamiar Aminian, Chris Todd, Clemens Becker.   

Abstract

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.
© 2017 S. Karger AG, Basel.

Entities:  

Keywords:  Fall detection; Falls; Inertial sensors; Recovery; Signal analysis

Mesh:

Year:  2017        PMID: 28848150     DOI: 10.1159/000478092

Source DB:  PubMed          Journal:  Gerontology        ISSN: 0304-324X            Impact factor:   5.140


  4 in total

Review 1.  [Prevention of falls and fall-related injuries : Personal balance and future tasks].

Authors:  Clemens Becker
Journal:  Z Gerontol Geriatr       Date:  2017-10-13       Impact factor: 1.281

2.  A Blockchain-Enabled Framework for mHealth Systems.

Authors:  Dragos Daniel Taralunga; Bogdan Cristian Florea
Journal:  Sensors (Basel)       Date:  2021-04-16       Impact factor: 3.576

3.  Feasibility of Using Floor Vibration to Detect Human Falls.

Authors:  Yu Shao; Xinyue Wang; Wenjie Song; Sobia Ilyas; Haibo Guo; Wen-Shao Chang
Journal:  Int J Environ Res Public Health       Date:  2020-12-29       Impact factor: 3.390

4.  Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.

Authors:  Luca Palmerini; Jochen Klenk; Clemens Becker; Lorenzo Chiari
Journal:  Sensors (Basel)       Date:  2020-11-13       Impact factor: 3.576

  4 in total

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