PDF(Pubmed)
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.
摘要：
肌肉骨骼损伤的康复侧重于重建和监测肌肉激活模式以准确地产生力。这项研究的目的是探索使用一种新型的低功率可穿戴分布式实时超声同步肌肉骨骼评估（SMART-US）设备来预测等距深蹲任务期间的力。参与者（N=5）在两种医学成像技术下进行了最大等距深蹲；在优势股外侧肌和股直肌上放置SMART-US传感器上的临床肌肉骨骼运动模式（m模式）超声，股外侧肌,内侧腿筋,和股内侧肌。提取超声特征,并采用线性岭回归模型预测地面反作用力。使用临床M模式测试超声特征预测测量力的性能，股外侧肌SMART-US传感器（SMART-US：VL），股直肌（SMART-US：RF），内侧腿筋（SMART-US：MH），和股内侧肌（SMART-US：VMO）或使用所有四个SMART-US传感器（分布式SMART-US）。模型训练表明临床M模式和分布式SMART-US模型均与SMART-US:VL有显著差异,SMART-US:MH,SMART-US:RF,和SMART-US：VMO模型（p<0.05）。模型验证表明，分布式SMART-US模型的R2为0.80±0.04，与SMART-US：VL有显著差异，但与临床M模式模型无显著差异。总之,一种新型的可穿戴分布式SMART-US系统可以使用机器学习来预测地面反作用力，证明了可穿戴超声成像用于地面反作用力估计的可行性。
