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Citing this Article

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Published on 21.08.17 in Vol 4, No 2 (2017): Jul-Dec

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

Works citing "Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation"

Martin O'Reilly, Joe Duffin, Tomas Ward, Brian Caulfield

JMIR Rehabil Assist Technol 2017 (Aug 21); 4(2):e9 

According to Crossref, the following articles are citing this article (DOI 10.2196/rehab.7259):

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

  1. O’Reilly M, Caulfield B, Ward T, Johnston W, Doherty C. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review. Sports Medicine 2018;48(5):1221
    CrossRef
  2. Lee J, Joo H, Lee J, Chee Y. Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach. Sensors 2020;20(2):361
    CrossRef
  3. Prabhu G, O’Connor NE, Moran K. Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models. Sensors 2020;20(17):4791
    CrossRef
  4. O'Reilly MA, Slevin P, Ward T, Caulfield B. A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift. JMIR mHealth and uHealth 2018;6(1):e33
    CrossRef
  5. Stubberud A, Omland PM, Tronvik E, Olsen A, Sand T, Linde M. Wireless Surface Electromyography and Skin Temperature Sensors for Biofeedback Treatment of Headache: Validation Study with Stationary Control Equipment. JMIR Biomedical Engineering 2018;3(1):e1
    CrossRef
  6. Pan D, Liu H, Qu D, Zhang Z, Lv J. Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM. Mobile Information Systems 2020;2020:1
    CrossRef
  7. Argent R, Bevilacqua A, Keogh A, Daly A, Caulfield B. The Importance of Real-World Validation of Machine Learning Systems in Wearable Exercise Biofeedback Platforms: A Case Study. Sensors 2021;21(7):2346
    CrossRef
  8. De Fazio R, Mastronardi VM, De Vittorio M, Visconti P. Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview. Sensors 2023;23(4):1856
    CrossRef
  9. Hart R, Smith H, Zhang Y. Systematic review of automatic assessment systems for resistance-training movement performance: A data science perspective. Computers in Biology and Medicine 2021;137:104779
    CrossRef
  10. Džaja D, Čibarić M, Šeketa G, Magjarević R. Accelerometer-based algorithm for the segmentation and classification of repetitive human movements during workouts. Automatika 2023;64(2):211
    CrossRef
  11. Cerfoglio S, Capodaglio P, Rossi P, Conforti I, D’Angeli V, Milani E, Galli M, Cimolin V. Evaluation of Upper Body and Lower Limbs Kinematics through an IMU-Based Medical System: A Comparative Study with the Optoelectronic System. Sensors 2023;23(13):6156
    CrossRef
  12. Hu X, Zhang W, Ou H, Mo S, Liang F, Liu J, Zhao Z, Qu X. Enhancing squat movement classification performance with a gated long-short term memory with transformer network model. Sports Biomechanics 2024;:1
    CrossRef
  13. Hart R, Smith H, Zhang Y. The development of an automated assessment system for resistance training movement. Sports Biomechanics 2024;:1
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/rehab.7259):

  1. Brennan L, Bevilacqua A, Kechadi T, Caulfield B. 8th European Medical and Biological Engineering Conference. 2021. Chapter 4:29
    CrossRef