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

Portable and Mobile Technologies for Rehabilitation (151) Wearable Devices and Sensors (888) Emerging Technologies for Rehabilitation (261) Fitness Trackers and Smart Pedometers/Accelerometers (561)

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

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

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

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

Authors of this article:

Martin O'Reilly1, 2 Author Orcid Image ;   Joe Duffin1 Author Orcid Image ;   Tomas Ward3, 4 Author Orcid Image ;   Brian Caulfield1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (15) Metrics

Journals

  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 View
  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 View
  3. Prabhu G, O’Connor N, 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 View
  4. O'Reilly M, 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 View
  5. Stubberud A, Omland P, 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 View
  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 View
  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 View
  8. De Fazio R, Mastronardi V, De Vittorio M, Visconti P. Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview. Sensors 2023;23(4):1856 View
  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 View
  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 View
  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 View
  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 View
  13. Hart R, Smith H, Zhang Y. The development of an automated assessment system for resistance training movement. Sports Biomechanics 2024:1 View

Books/Policy Documents

  1. Brennan L, Bevilacqua A, Kechadi T, Caulfield B. 8th European Medical and Biological Engineering Conference. View