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Citing this Article

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Published on 11.10.17 in Vol 5, No 10 (2017): October

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

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

Nicholas Shawen, Luca Lonini, Chaithanya Krishna Mummidisetty, Ilona Shparii, Mark V Albert, Konrad Kording, Arun Jayaraman

JMIR Mhealth Uhealth 2017 (Oct 11); 5(10):e151 

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.8201):

(note that this is only a small subset of citations)

  1. Boe AJ, McGee Koch LL, O’Brien MK, Shawen N, Rogers JA, Lieber RL, Reid KJ, Zee PC, Jayaraman A. Automating sleep stage classification using wireless, wearable sensors. npj Digital Medicine 2019;2(1)
    CrossRef
  2. Bhakta K, Camargo J, Donovan L, Herrin K, Young A. Machine Learning Model Comparisons of User Independent & Dependent Intent Recognition Systems for Powered Prostheses. IEEE Robotics and Automation Letters 2020;5(4):5393
    CrossRef
  3. Sczuka KS, 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
    CrossRef
  4. Albert MV, Sugianto A, Nickele K, Zavos P, Sindu P, Ali M, Kwon S. Hidden Markov model-based activity recognition for toddlers. Physiological Measurement 2020;41(2):025003
    CrossRef
  5. Chadwell A, Diment L, Micó-Amigo M, Morgado Ramírez DZ, 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)
    CrossRef
  6. Hewitt MA, Smith DG, Heckman JT, Pasquina PF. COVID‐19: A catalyst for change in virtual health care utilization for persons with limb loss. PM&R 2021;13(6):637
    CrossRef
  7. Tang X, Yu S, Chu J, Fan H. Damaged/missing proximity sensor induces screen mistouch when answering calls: Prediction of smartphone answering status by posture data. Journal of Intelligent & Fuzzy Systems 2021;41(1):1963
    CrossRef
  8. Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors 2021;21(15):5134
    CrossRef
  9. Harari Y, Shawen N, Mummidisetty CK, Albert MV, Kording KP, 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)
    CrossRef
  10. Sawers A, McDonald CL, Hafner BJ, Eshraghi A. A survey for characterizing details of fall events experienced by lower limb prosthesis users. PLOS ONE 2022;17(7):e0272082
    CrossRef
  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
    CrossRef
  12. Sok P, Xiao T, Azeze Y, Jayaraman A, Albert MV. Activity Recognition for Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models. IEEE Sensors Journal 2018;18(15):6369
    CrossRef
  13. Santoyo-Ramón JA, Casilari-Pérez E, Cano-García JM. A study on the impact of the users’ characteristics on the performance of wearable fall detection systems. Scientific Reports 2021;11(1)
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  18. Bube B, Zanón BB, Lara Palma AM, Klocke H. Wearable Devices in Diving: Scoping Review. JMIR mHealth and uHealth 2022;10(9):e35727
    CrossRef
  19. Botonis OK, Harari Y, Embry KR, Mummidisetty CK, Riopelle D, Giffhorn M, Albert MV, Heike V, Jayaraman A. Wearable airbag technology and machine learned models to mitigate falls after stroke. Journal of NeuroEngineering and Rehabilitation 2022;19(1)
    CrossRef
  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
    CrossRef
  21. Choo YJ, Chang MC. Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review. Prosthetics & Orthotics International 2023;47(3):226
    CrossRef
  22. Finco MG, Sumien N, Moudy SC. 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
    CrossRef
  23. Galey L, Fuentes O, Gonzalez RV. Transfemoral Amputee Stumble Detection through Machine-Learning Classification: Initial Exploration with Three Subjects. Prosthesis 2024;6(2):235
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.8201):

  1. Xiao T, Albert MV. Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. 2021. Chapter 2:11
    CrossRef