Stumbling prediction based on plantar pressure distribution.
BACKGROUND: Stumbles are common accidents that can result in falls and serious injuries, particularly in the workplace where back and forth movements are involved and in offices where high heels are imperative. Currently, the characteristics of plantar pressure during a stumble and the differences b...
| Published in: | Work Vol. 64; no. 4; pp. 705 - 713 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141230226&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141230226 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2019 vid: 64 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 141230226 141230226 141230226 10.3233/WOR-193032 141230226 ppf: 705 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Stumbling prediction based on plantar pressure distribution. aug: au: Niu, Jianwei Zheng, Yanling Liu, Haixiao Chen, Xiao Ran, Linghua affil: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China sug: subj: Gait Disorders, Neurologic Therapy Occupational-Related Injuries Therapy Occupational Safety Accidental Falls Prevention and Control Data Mining Methods Gait Analysis Artificial Intelligence Male Adult Calibration Data Analysis Software Pressure (Physiology) Human Funding Source Adult: 19-44 years Male ab: BACKGROUND: Stumbles are common accidents that can result in falls and serious injuries, particularly in the workplace where back and forth movements are involved and in offices where high heels are imperative. Currently, the characteristics of plantar pressure during a stumble and the differences between stumbling and a normal gait remain unclear. OBJECTIVE: This paper is aimed at providing insights into the feasibility of the data mining technique for interventions in stumble-related occupational safety issues. METHODS: The characteristics of plantar pressure distribution during stumbling and normal gait were analyzed by using the power spectrum density (PSD) and the Support Vector Machine (SVM). The PSD, a novel pattern recognition feature, was used to mathematically describe the image signal. The SVM, a powerful data mining technique, was used as the classifier to recognize a stumble. Dynamic plantar pressures were measured from twelve healthy participants as they walked. RESULTS: The plantar pressures of the stumbling gaits had significantly different patterns compared to the normal ones, from either a qualitative or quantitative perspective. The mean recognition accuracy of the proposed method reached 96.7%. CONCLUSIONS: This study helps better understand stumbles and provides a theoretical basis for stumble-related occupational injuries. In addition, the stumble is the precursor of a fall and the research on stumble recognition would be of value to predict and provide warnings of falls and to design anti-fall devices for potential victims. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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