King, E. L.; Patwardhan, S.; Bashatah, A.; Magee, M.; Jones, M. T.; Wei, Q.; Sikdar, S.; Chitnis, P. V. Distributed Wearable Ultrasound Sensors Predicts Isometric Ground Reaction Force. Preprints2024, 2024070616. https://doi.org/10.20944/preprints202407.0616.v1
APA Style
King, E. L., Patwardhan, S., Bashatah, A., Magee, M., Jones, M. T., Wei, Q., Sikdar, S., & Chitnis, P. V. (2024). Distributed Wearable Ultrasound Sensors Predicts Isometric Ground Reaction Force. Preprints. https://doi.org/10.20944/preprints202407.0616.v1
Chicago/Turabian Style
King, E. L., Siddhartha Sikdar and Parag V Chitnis. 2024 "Distributed Wearable Ultrasound Sensors Predicts Isometric Ground Reaction Force" Preprints. https://doi.org/10.20944/preprints202407.0616.v1
Abstract
Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N=5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis(SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US:RF, and SMART-US:VMO models(p<0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ±0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.
Keywords
wearable ultrasound; force production; neuromuscular monitoring
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
