Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force.
Sensors (Basel)
; 24(15)2024 Aug 03.
Article
em En
| MEDLINE
| ID: mdl-39124070
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Ultrassonografia
/
Dispositivos Eletrônicos Vestíveis
/
Contração Isométrica
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article