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Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p< 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems.

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Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.

Recently, wavelet analysis has proved to be one of the most powerful signal processing tools for the analysis of surface electromyography (sEMG) signals. It has been widely used in sEMG pattern classification for both clinical and engineering applications. This study investigated the usefulness of extracting sEMG features from multiple-level wavelet decomposition and reconstruction. A suitable wavelet based function was used to yield useful resolution components from the sEMG signal. The optimal sEMG resolution component was selected and then its reconstruction carried out. Throughout this process, noise and unwanted sEMG components were removed. Effective sEMG components were extracted with twenty-five state-of-the-art features in both the time domain and the frequency domain. Two criteria were deployed in the evaluation, scatter graphs and a class separation index. The experimental results show that most sEMG features extracted from the reconstructed sEMG signal of the first and second-level wavelet detail coefficients yield improved class separability in feature space. Some features extracted from the sub-signals are recommended such as the myopulse percentage rate, zero crossing, Willison amplitude and the mean absolute value. The proposed method will ensure that the classification accuracy will be as high as possible while the computational time will be as low as possible. Ill. 3, bibl. 24, tabl. 2 (in English; abstracts in English and Lithuanian). A. Phinyomark, A. Nuidod, P. Phukpattaranont, C. Limsakul. Požymių išskyrimas ir vilnelių transformacijos koeficientų sumažinimas elektromiografijos atvaizdams klasifikuoti // Elektronika ir elektrotechnika. -Kaunas: Technologija, 2012. -Nr. 6(122). -P. 27-32.Vilnelių transformacija yra vienas iš geriausių signalų apdorojimo įrankių atliekant paviršinės elektromiografijos (pEMG) signalų analizę. Ji plačiai naudojama klasifikuojant pEMG atvaizdus tiek klinikinėse, tiek inžinerinėse taikomosiose programose. Panaudota tinkama vilnelių funkcija siekiant gauti tinkamos rezoliucijos komponentus iš pEMG signalo. Buvo parinktas optimalus pEMG rezoliucijos komponentas ir atlikta jos rekonstrukcija. Šio proceso metu buvo pašalintas triukšmas ir nepageidaujami pEMG komponentai. Eksperimentiniai rezultatai parodė, kad dauguma pEMG bruožų, išskirtų iš rekonstruoto pEMG signalo, padeda geriau atskirti klases bruožų erdvėje. Pasiūlytas metodas užtikrina kiek įmanoma didesnį klasifikacijos tikslumą ir mažesnę skaičiavimo trukmę. Il. 3, bibl. 24, lent. 2 (anglų kalba; santraukos anglų ir lietuvių k.).

Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle “big data”. Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V’s definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called “topological data analysis” and directions for future research are outlined and discussed.