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Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

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  • Fabio Bagalà
  • Clemens Becker
  • Angelo Cappello
  • Lorenzo Chiari
  • Kamiar Aminian
  • Jeffrey M Hausdorff
  • Wiebren Zijlstra
  • Jochen Klenk

Abstract

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

Suggested Citation

  • Fabio Bagalà & Clemens Becker & Angelo Cappello & Lorenzo Chiari & Kamiar Aminian & Jeffrey M Hausdorff & Wiebren Zijlstra & Jochen Klenk, 2012. "Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
  • Handle: RePEc:plo:pone00:0037062
    DOI: 10.1371/journal.pone.0037062
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    Cited by:

    1. Cheng-Wen Lee & Hsiu-Mang Chuang, 2021. "Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-1.
    2. Carlos Medrano & Raul Igual & Inmaculada Plaza & Manuel Castro, 2014. "Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    3. José Carlos Castillo & Davide Carneiro & Juan Serrano-Cuerda & Paulo Novais & Antonio Fernández-Caballero & José Neves, 2014. "A multi-modal approach for activity classification and fall detection," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(4), pages 810-824, April.
    4. Dave Archer & Michael A August & Georgios Bouloukakis & Christopher Davison & Mamadou H Diallo & Dhrubajyoti Ghosh & Christopher T Graves & Michael Hay & Xi He & Peeter Laud & Steve Lu & Ashwin Machan, 2022. "Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy," The Journal of Defense Modeling and Simulation, , vol. 19(3), pages 501-517, July.
    5. Melissa C Kilby & Semyon M Slobounov & Karl M Newell, 2014. "Postural Instability Detection: Aging and the Complexity of Spatial-Temporal Distributional Patterns for Virtually Contacting the Stability Boundary in Human Stance," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.
    6. Ionut Anghel & Tudor Cioara & Dorin Moldovan & Marcel Antal & Claudia Daniela Pop & Ioan Salomie & Cristina Bianca Pop & Viorica Rozina Chifu, 2020. "Smart Environments and Social Robots for Age-Friendly Integrated Care Services," IJERPH, MDPI, vol. 17(11), pages 1-31, May.

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