Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Threshold-based Pre-impact Fall Detection and its Validation Using the Real-world Elderly Dataset

임계값 기반 충격 전 낙상검출 및 실제 노인 데이터셋을 사용한 검증

  • Dongkwon Kim (Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University) ;
  • Seunghee Lee (Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University) ;
  • Bummo Koo (Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University) ;
  • Sumin Yang (Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University) ;
  • Youngho Kim (Department of Biomedical Engineering and Institute of Medical Engineering, Yonsei University)
  • 김동권 (연세대학교 의공학과, 연세대학교 의료공학연구소) ;
  • 이승희 (연세대학교 의공학과, 연세대학교 의료공학연구소) ;
  • 구범모 (연세대학교 의공학과, 연세대학교 의료공학연구소) ;
  • 양수민 (연세대학교 의공학과, 연세대학교 의료공학연구소) ;
  • 김영호 (연세대학교 의공학과, 연세대학교 의료공학연구소)
  • Received : 2023.10.24
  • Accepted : 2023.11.14
  • Published : 2023.12.31
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Abstract

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

Keywords

Acknowledgement

본 연구는 산업통상자원부와 한국산업기술진흥원의 "R&D재발견프로젝트"의 지원을 받아 수행된 연구결과이며(과제번호: P0026060) 또한 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신 사업의 결과입니다(2022RIS-005).

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