Quantify work load and muscle functional activation patterns in neck-shoulder muscles of female sewing machine operators using surface electromyogram

Background Work-related musculoskeletal disorders （WMSDs） have high prevalence in sewing machine operators employed in the garment industry. Long work duration, sustained low level work and precise hand work are the main risk factors of neck-shoulder disorders for sewing machine operators. Surface electromyogram （sEMG） offers a valuable tool to determine muscle activity （internal exposure） and quantify muscular load （external exposure）. During sustained and/or repetitive muscle contractions, typical changes of muscle fatigue in sEMG, as an increase in amplitude or a decrease as a shift in spectrum towards lower frequencies, can be observed. In this paper, we measured and quantified the muscle load and muscular activity patterns of neck-shoulder muscles in female sewing machine operators during sustained sewing machine operating tasks using sEMG. Methods A total of 18 healthy women sewing machine operators volunteered to participate in this study. Before their daily sewing machine operating task, we measured the maximal voluntary contractions （MVC） and 20%MVC of bilateral cervical erector spinae （CES） and upper trapezius （UT） respectively, then the sEMG signals of bilateral UT and CES were monitored and recorded continuously during 200 minutes of sustained sewing machine operating simultaneously which equals to 20 time windows with 10 minutes as one time window. After 200 minutes＇ work, we retest 20%MVC of four neck-shoulder muscles and recorded the sEMG signals. Linear analysis, including amplitude probability distribution frequency （APDF）, amplitude analysis parameters such as roof mean square （RMS） and spectrum analysis parameter as median frequency （MF）, were used to calculate and indicate muscle load and muscular activity of bilateral CES and UT. Results During 200 minutes of sewing machine operating, the median load for the left cervical erector spinae （LCES）, right cervical erector spinae （RCES）, left upper trapezius （LUT） and right upper trapezius （RUT） were 6.78%MVE, 6.94%MVE, 6.47%MVE and 5.68%MVE, respectively. Work load of right muscles are significantly higher than that of the left muscles （P〈0.05）; sEMG signal analysis of isometric contractions indicated that the amplitude value before operating was significantly higher than that of after work （P 〈0.01）, and the spectrum value of bilateral CES and UT were significantly lower than those of after work （P 〈0.01）; according to the sEMG signal data of 20 time windows, with operating time pass by, the muscle activity patterns of bilateral CES and UT showed dynamic changes, the maximal amplitude of LCES, RCES, LUT occurred at the 20th time window, RUT at 16th time window, spectrum analysis showed that the lower value happened at 7th, 16th, 20th time windows. Conclusions Female sewing machine operators were exposed to high sustained static load on bilateral neck-shoulder muscles; left neck and shoulder muscles were held in more static positions; the 7th, 16th, and 20th time windows were muscle fatiQue period that erQonomics intervention can protocol at these periods.

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

Spinal orthoses were designed to correct poor posture;however,they may restrict trunk movements at all times,making daily activities difficult.Detecting trunk movements can provide instructions for adjusting the stiffness of the spinal orthosis.This study evaluated the feasibility of identifying movements based on surface electromyography(sEMG)signals.Ten participants were tested for different movements with two different modalities:motion without the spinal orthosis(Normal)and with the spinal orthosis(Spinal orthosis).The sEMG signals were collected from eight muscles using surface electrodes during four movements[flexion-extension,lateral bending,axial rotation,and stand to sit to stand].Four time domain features were extracted,with a total of 32 feature vectors.The principal component analysis(PCA)method was adopted to feature selection,and it was found that eight feature dimensions can make cumulative explained variance exceed 95%.The results showed that machine learning algorithms could not only identify Normal and Spinal orthosis movement modalities,but also distinguish four daily movements.Moreover,the classification performance of Random Forest(RF),k-Nearest Neighbor(kNN),and Support Vector Machine(SVM)algorithms were also compared.The results showed that all three machine algorithms have high classification accuracy.The machine learning methods can accurately identify movement patterns by considering sEMG signals,which may provide instructions for adjusting the stiffness of the spinal orthosis.In the future,the spinal orthosis with adjustable stiffness controlled by sEMG signals could help correct poor posture,and permit the wearer to achieve free movement when needed.

Timing and Classication of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational detection.The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals.Understanding muscle activation timing allows identication of muscle locations and feature validation for precise modeling.This work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern classication.To this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going upstairs.Onset,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication model.The proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space.The activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation measurements.The fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art classiers.The proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.

Change of bio-electric interferential currents of acute fatigue and recovery in male sprinters

We studied the muscle fatigue and recovery of thirty male sprinters(aged 18–22 years)using the Frequency Analysis Method(FAM).The interferential currents(ICs)with different thresholds for sensory,motor and pain responses,the maximal voluntary contraction(MVC),and the amplitude of the surface EMG(aEMG,sEMG)were assessed prior to and immediately after an acute explosive fatigue training session,and during one-week recovery.We found that IC increased on average from 32.38.9 mA to 37.57.5 mA in sensory response at 10 Hz immediately post training(p=0.004)but decreased at 24-hr post training(p=0.008)and returned to pre-levels thereafter.Motor and pain response patterns at 10 Hz were similar(motor:p=0.033 and 0.040;pain:p=0.022 and 0.019,respectively).The change patterns of ICs were similar to but prior to the changes of sEMG.The agreement between IC assessment and amplitude of sEMG(aEMG)/MVC ratio was good(>95%).The present study suggested that the changes in ICs were prior to the changes in both the aEMG and force during fatigue.These changes may reflect the physiological sensory change due to peripheral fatigue.FAM may be useful as an effective early detection and simple tool for monitoring muscle fatigue during training and recovery in athletes.