Trunk compensation electromyography features purification and classification model using generative adversarial network

Chen, Yan; Ma, Ke; Yang, Lie; Yu, Shi; Cai, Siqi; Xie, Longhan

doi:10.1016/j.bspc.2020.102345

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…The parameters for measuring trunk compensation in the sagittal plane included trunk angular displacement (12/41, 29% studies), trunk linear displacement (8/41, 20% studies), trunk contribution slope [ 37 , 38 , 62 ], acceleration of trunk motion [ 28 , 64 ], surface electromyogram (sEMG) signals [ 39 , 77 ], and face orientation [ 67 ]. The parameters used to measure trunk compensation in the transverse plane included trunk angular displacement (7/24, 29% studies), acceleration of trunk motion [ 27 , 28 , 64 ], trunk linear displacement [ 34 , 40 ], and sEMG signal [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

“…Physiological signal sensing technologies include electromyogram (8/72, 11% studies), electroencephalogram (EEG) [ 33 , 80 ], and fMRI [ 71 ] systems. According to the reviewed studies, sEMG signals of upper limb muscles (including, but not limited to, biceps, triceps, upper trapezius, pectoralis major, brachioradialis, anterior, middle, and posterior deltoids) and trunk muscles (left or right rectus abdominis, left or right obliquus externus abdominis, left or right thoracic erector spinae, left or right lumbar erector spinae, and descending part of the trapezius) not only helped to discriminate true recovery and compensation [ 29 , 30 , 33 , 84 ] but also could be used as features for automatic compensation detection [ 39 , 53 , 77 ]. Chen et al [ 77 ] confirmed that using a generative adversarial network with sEMG signals as features could achieve excellent detection performance (accuracy=94.58%, +1.15% to –1.15%) of trunk compensatory movements.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 15 studies used AI-based methods to detect compensatory postures, and 9 studies classified 3 common compensatory postures: trunk lean forward, trunk rotation, and shoulder elevation [ 45 , 48 - 50 , 53 , 54 , 74 , 77 , 91 ]. In addition, 4 studies discriminated trunk compensatory movements in the sagittal, transverse, and coronal planes [ 43 , 44 , 55 , 61 ], and 2 studies did not mention the type of compensatory posture that was classified [ 78 , 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…Notably, 8 studies [ 44 , 48 , 49 , 54 , 61 , 74 , 77 , 79 ] used more than one ML algorithm to compare the classification results for compensatory postures. For example, Zhi et al [ 48 ] used both SVM and recurrent neural network classifiers to classify shoulder elevation, trunk rotation, and lean forward.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Wang¹,

Fu²,

Ye³

et al. 2022

J Med Internet Res

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Background Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation. Objective This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation. Methods We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question. Results A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures. Conclusions This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Wang¹,

Fu²,

Ye³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Diverse machine learning algorithms are utilized to establish the relationship between sEMG and the joint angle ( Chen et al, 2021 ; Padhy 2021 ; Xue et al, 2021 ), whose process is easy and no complex calculations are involved. For example, Xiao et al ( Xiao et al, 2017 ) used the average absolute value, waveform length, zero-crossing points, and the number of slope sign changes to extract time-domain features, and they proposed a gray feature-weighted support vector machine to construct models of sEMG and elbow joint angles.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

Zhang

Long

Zhao

et al. 2022

Front. Bioeng. Biotechnol.

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The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time–frequency domain was proposed. As the degree of freedom (DOF) of the pronation–supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation–supination movements.

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Classification of human movements with and without spinal orthosis based on surface electromyogram signals

Wang

Li²,

Guo³

et al. 2022

Medicine in Novel Technology and Devices

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Trunk compensation electromyography features purification and classification model using generative adversarial network

Cited by 8 publications

References 35 publications

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

Classification of human movements with and without spinal orthosis based on surface electromyogram signals

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