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King EL, Patwardhan S, Bashatah A, Magee M, Jones MT, Wei Q, Sikdar S, Chitnis PV. Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force. SENSORS (BASEL, SWITZERLAND) 2024; 24:5023. [PMID: 39124070 PMCID: PMC11314925 DOI: 10.3390/s24155023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 07/24/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
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.
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Affiliation(s)
- Erica L. King
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA 22030, USA
- Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA 22030, USA;
| | - Shriniwas Patwardhan
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA 22030, USA
- National Institute of Health, Bethesda, MD 20892, USA
| | - Ahmed Bashatah
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
| | - Meghan Magee
- School of Kinesiology, George Mason University, Fairfax, VA 22030, USA;
- School of Sports, Recreation and Tourism Management, George Mason University, Fairfax, VA 22030, USA
- School of Health Sciences, Kent State University, Kent, OH 44240, USA
| | - Margaret T. Jones
- Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA 22030, USA;
- School of Kinesiology, George Mason University, Fairfax, VA 22030, USA;
- School of Sports, Recreation and Tourism Management, George Mason University, Fairfax, VA 22030, USA
| | - Qi Wei
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
| | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA 22030, USA
| | - Parag V. Chitnis
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA; (S.P.); (A.B.); (Q.W.); (S.S.)
- Center for Adaptive Systems of Brain-Body Interactions, George Mason University, Fairfax, VA 22030, USA
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Freitag L, Hohenauer E, Meichtry A, Pauli C, Sommer B, Graf E. Effect of submaximal running in rocker shoes on gluteal muscle activation under different running conditions. Sci Sports 2022. [DOI: 10.1016/j.scispo.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Pakniyat N, Namazi H. Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations. Front Hum Neurosci 2021; 15:749082. [PMID: 34690727 PMCID: PMC8531105 DOI: 10.3389/fnhum.2021.749082] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/06/2021] [Indexed: 11/18/2022] Open
Abstract
In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.
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Affiliation(s)
| | - Hamidreza Namazi
- Incubator of Kinanthropology Research, Faculty of Sports Studies, Masaryk University, Brno, Czechia.,College of Engineering and Science, Victoria University, Melbourne, VIC, Australia
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Kim S, Lee S, Jeong W. EMG Measurement with Textile-Based Electrodes in Different Electrode Sizes and Clothing Pressures for Smart Clothing Design Optimization. Polymers (Basel) 2020; 12:polym12102406. [PMID: 33086662 PMCID: PMC7603359 DOI: 10.3390/polym12102406] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/15/2020] [Accepted: 10/17/2020] [Indexed: 02/07/2023] Open
Abstract
The surface electromyography (SEMG) is one of the most popular bio-signals that can be applied in health monitoring systems, fitness training, and rehabilitation devices. Commercial clothing embedded with textile electrodes has already been released onto the market, but there is insufficient information on the performance of textile SEMG electrodes because the required configuration may differ according to the electrode material. The current study analyzed the influence of electrode size and pattern reduction rate (PRR), and hence the clothing pressure (Pc) based on in vivo SEMG signal acquisition. Bipolar SEMG electrodes were made in different electrode diameters Ø 5–30 mm, and the clothing pressure ranged from 6.1 to 12.6 mmHg. The results supported the larger electrodes, and Pc showed better SEMG signal quality by showing lower baseline noise and a gradual increase in the signal to noise ratio (SNR). In particular, electrodes, Ø ≥ 20 mm, and Pc ≥ 10 mmHg showed comparable performance to Ag-Ag/Cl electrodes in current textile-based electrodes. The current study emphasizes and discusses design factors that are particularly required in the designing and manufacturing process of smart clothing with SEMG electrodes, especially as an aspect of clothing design.
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