This paper is in the following e-collection/theme issue:

mHealth for Telemedicine and Homecare (418) mHealth for Data Collection and Research (981) mHealth for Wellness, Behavior Change and Prevention (2922) Machine Learning (1501) mHealth for Rehabilitation (269)

Published on in Vol 5, No 10 (2017): October

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications

Authors of this article:

Nicholas Shawen1, 2 Author Orcid Image ;   Luca Lonini1, 2, 3 Author Orcid Image ;   Chaithanya Krishna Mummidisetty1 Author Orcid Image ;   Ilona Shparii1, 2, 4 Author Orcid Image ;   Mark V Albert1, 2, 3, 4 Author Orcid Image ;   Konrad Kording5, 6 Author Orcid Image ;   Arun Jayaraman1, 2, 3, 7 Author Orcid Image
Article Authors Cited by (30) Tweetations (15) Metrics

Journals

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  3. Sczuka K, Schwickert L, Becker C, Klenk J. Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study. Journal of Medical Internet Research 2020;22(4):e13961 View
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  5. Chadwell A, Diment L, Micó-Amigo M, Morgado Ramírez D, Dickinson A, Granat M, Kenney L, Kheng S, Sobuh M, Ssekitoleko R, Worsley P. Technology for monitoring everyday prosthesis use: a systematic review. Journal of NeuroEngineering and Rehabilitation 2020;17(1) View
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  8. Usmani S, Saboor A, Haris M, Khan M, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors 2021;21(15):5134 View
  9. Harari Y, Shawen N, Mummidisetty C, Albert M, Kording K, Jayaraman A. A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls. Journal of NeuroEngineering and Rehabilitation 2021;18(1) View
  10. Sawers A, McDonald C, Hafner B, Eshraghi A. A survey for characterizing details of fall events experienced by lower limb prosthesis users. PLOS ONE 2022;17(7):e0272082 View
  11. Yan G, Li J, Xie H, Zhou M, Souri A. Adaptive Control System of Intelligent Lower Limb Prosthesis Based on 5G Virtual Reality. Wireless Communications and Mobile Computing 2022;2022:1 View
  12. Sok P, Xiao T, Azeze Y, Jayaraman A, Albert M. Activity Recognition for Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models. IEEE Sensors Journal 2018;18(15):6369 View
  13. Santoyo-Ramón J, Casilari-Pérez E, Cano-García J. A study on the impact of the users’ characteristics on the performance of wearable fall detection systems. Scientific Reports 2021;11(1) View
  14. Mellema M, Gjøvaag T. Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review. Sensors 2022;22(6):2243 View
  15. Nishio K, Kaburagi T, Hamada Y, Matsumoto T, Kumagai S, Kurihara Y. Construction of an Aggregated Fall Detection Model Utilizing a Microwave Doppler Sensor. IEEE Internet of Things Journal 2022;9(3):2044 View
  16. Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR mHealth and uHealth 2021;9(9):e24402 View
  17. Monaco V, Aprigliano F, Palmerini L, Palumbo P, Chiari L, Micera S. Biomechanical Measures for Fall Risk Assessment and Fall Detection in People with Transfemoral Amputations for the Next-Generation Prostheses: A Scoping Review. JPO Journal of Prosthetics and Orthotics 2022;34(3):e144 View
  18. Bube B, Zanón B, Lara Palma A, Klocke H. Wearable Devices in Diving: Scoping Review. JMIR mHealth and uHealth 2022;10(9):e35727 View
  19. Botonis O, Harari Y, Embry K, Mummidisetty C, Riopelle D, Giffhorn M, Albert M, Heike V, Jayaraman A. Wearable airbag technology and machine learned models to mitigate falls after stroke. Journal of NeuroEngineering and Rehabilitation 2022;19(1) View
  20. Demeco A, Frizziero A, Nuresi C, Buccino G, Pisani F, Martini C, Foresti R, Costantino C. Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors. Sensors 2023;23(4):1880 View
  21. Choo Y, Chang M. Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review. Prosthetics & Orthotics International 2023;47(3):226 View
  22. Finco M, Sumien N, Moudy S. Clinical evaluation of fall risk in older adults who use lower‐limb prostheses: A scoping review. Journal of the American Geriatrics Society 2023;71(3):959 View
  23. Galey L, Fuentes O, Gonzalez R. Transfemoral Amputee Stumble Detection through Machine-Learning Classification: Initial Exploration with Three Subjects. Prosthesis 2024;6(2):235 View
  24. Liu N, Liu X, Su Z, Wang J. Research on the Gait Phase Analysis Method of Femoral Amputation Patients Based on the Thin Film Pressure Sensor. Wireless Personal Communications 2024;137(2):1233 View
  25. Patricks V, Twiste M, Granat M, Curtin S, Worsley P. Clinicians’ Perspectives of Activity Monitoring Applications and Facilitators for Lower-Limb Prosthetic Service Provision. JPO Journal of Prosthetics and Orthotics 2024 View

Books/Policy Documents

  1. Xiao T, Albert M. Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. View