論文

査読有り
2022年4月

Identification of the cause of fall during the pre-impact fall period.

Journal of physical therapy science
  • Sho Sasaki
  • ,
  • Hiroaki Yamamoto
  • ,
  • Kodai Kitagawa
  • ,
  • Chikamune Wada

34
4
開始ページ
320
終了ページ
326
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1589/jpts.34.320

[Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant's waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0-2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method.

リンク情報
DOI
https://doi.org/10.1589/jpts.34.320
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/35400837
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989476
ID情報
  • DOI : 10.1589/jpts.34.320
  • PubMed ID : 35400837
  • PubMed Central 記事ID : PMC8989476

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