Quantitative Elbow Spasticity Measurement Based on Muscle Activation Estimation Using Maximal Voluntary Contraction

Wang, Chao; Sivan, Manoj; Wang, Danyang; Zhang, Zhiqiang; Li, Gu-Qiang; Bao, Tianzhe; Xie, Sheng Quan

doi:10.1109/tim.2022.3173273

Cited by 5 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 204 ] In addition to IMU and EMG, Wang adds an additional pressure sensor placed between the hand and the table, quantifying the maximum voluntary elbow contraction task, when the patient lifts the table with one hand. [ 200 ] The system's assessment of muscle activation is 96% accurate, well above the 71% achieved with EMG alone.…”

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

“…[ 204 ] On this basis, some new indicators are proposed, such as the novel indicator based on PAC between angular velocity and sEMG amplitude, [ 199 ] and identifying the disparity between the impaired and unaffected side in the MVC test. [ 200 ] Zhang proposes a brand new evaluation indicator, a five‐dimensional vector that simultaneously integrates TSRT from the lambda model and four biomarkers from the kinematic model, realizing the complementation of the two. [ 203 ]…”

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

show abstract

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

Section: Multimodal Sensing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

show abstract

“…It is necessary to measure the maximum EMG signal of all subjects to homogenize their EMG signals. The most popular method is called MVC normalization, referring to a maximum voluntary contraction (MVC) performed prior to the test trials [84]. Typically, MVC contractions are performed against static resistance.…”

Section: ) Muscular Activationmentioning

confidence: 99%

Physiological and kinematic effects of a parallel rehabilitation robot (PWRR) on wrist movements

Zhang¹,

Yu²,

Su³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…HD-EMG signals are collected via arrays consisting of numerous (10–100) electrodes. HD-EMG has been applied to numerous fields involving muscle contraction, including kinematics [ 1 ], rehabilitation [ 2 , 3 ], diagnostics [ 2 ], user authentication [ 4 ], and control of prosthetic devices [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

Farago

Chan

2023

Sensors

View full text Add to dashboard Cite

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.

show abstract

Quantitative Elbow Spasticity Measurement Based on Muscle Activation Estimation Using Maximal Voluntary Contraction

Cited by 5 publications

References 39 publications

Multimodal Sensing in Stroke Motor Rehabilitation

Multimodal Sensing in Stroke Motor Rehabilitation

Physiological and kinematic effects of a parallel rehabilitation robot (PWRR) on wrist movements

Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements

Contact Info

Product

Resources

About