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Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets

Author

Listed:
  • Omar Aziz
  • Jochen Klenk
  • Lars Schwickert
  • Lorenzo Chiari
  • Clemens Becker
  • Edward J Park
  • Greg Mori
  • Stephen N Robinovitch

Abstract

Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets.

Suggested Citation

  • Omar Aziz & Jochen Klenk & Lars Schwickert & Lorenzo Chiari & Clemens Becker & Edward J Park & Greg Mori & Stephen N Robinovitch, 2017. "Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
  • Handle: RePEc:plo:pone00:0180318
    DOI: 10.1371/journal.pone.0180318
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    Cited by:

    1. Jonatha Sousa Pimentel & Raydonal Ospina & Anderson Ara, 2021. "Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining," Stats, MDPI, vol. 4(3), pages 1-19, August.
    2. Jesús Fernández-Bermejo Ruiz & Javier Dorado Chaparro & Maria José Santofimia Romero & Félix Jesús Villanueva Molina & Xavier del Toro García & Cristina Bolaños Peño & Henry Llumiguano Solano & Sara C, 2022. "Bedtime Monitoring for Fall Detection and Prevention in Older Adults," IJERPH, MDPI, vol. 19(12), pages 1-32, June.
    3. Sadik Kamel Gharghan & Saleem Latteef Mohammed & Ali Al-Naji & Mahmood Jawad Abu-AlShaeer & Haider Mahmood Jawad & Aqeel Mahmood Jawad & Javaan Chahl, 2018. "Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network," Energies, MDPI, vol. 11(11), pages 1-32, October.

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