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

Emerging Technologies for Rehabilitation (241) Wearable Devices and Sensors (816) Portable and Mobile Technologies for Rehabilitation (142) New Methods (908)

Published on in Vol 8, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29769, first published .
Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

Authors of this article:

Siavash Khaksar1 Author Orcid Image ;   Huizhu Pan1 Author Orcid Image ;   Bita Borazjani1 Author Orcid Image ;   Iain Murray1 Author Orcid Image ;   Himanshu Agrawal1 Author Orcid Image ;   Wanquan Liu2 Author Orcid Image ;   Catherine Elliott3 Author Orcid Image ;   Christine Imms4 Author Orcid Image ;   Amity Campbell3 Author Orcid Image ;   Corrin Walmsley3 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

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  2. Khaksar S, Pieters S, Borazjani B, Hyde J, Booker H, Khokhar A, Murray I, Campbell A. Posture Monitoring and Correction Exercises for Workers in Hostile Environments Utilizing Non-Invasive Sensors: Algorithm Development and Validation. Sensors 2022;22(24):9618 View
  3. den Hartog D, van der Krogt M, van der Burg S, Aleo I, Gijsbers J, Bonouvrié L, Harlaar J, Buizer A, Haberfehlner H. Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. Sensors 2022;22(12):4386 View
  4. Galasso S, Baptista R, Molinara M, Pizzocaro S, Calabrò R, De Nunzio A. Predicting physical activity levels from kinematic gait data using machine learning techniques. Engineering Applications of Artificial Intelligence 2023;123:106487 View
  5. Moghadam S, Yeung T, Choisne J. The Effect of IMU Sensor Location, Number of Features, and Window Size on a Random Forest Model’s Accuracy in Predicting Joint Kinematics and Kinetics During Gait. IEEE Sensors Journal 2023;23(22):28328 View
  6. Petersen B, Erickson K, Kurowski B, Boninger M, Treble-Barna A. Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
  7. Mohammadi Moghadam S, Ortega Auriol P, Yeung T, Choisne J. 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units. Frontiers in Bioengineering and Biotechnology 2024;12 View
  8. Mishra A, Singh P, Chauhan N, Roy S, Tiwari A, Gupta S, Tiwari A, Patra S, Das T, Mishra P, Nejad A, Shukla Y, Jain U, Tiwari A. Emergence of integrated biosensing-enabled digital healthcare devices. Sensors & Diagnostics 2024;3(5):718 View