Expert committee classifier for hand motions recognition from EMG signals

Abstract

This paper presents the design and implementation of a novel technique for the recognition of four hand motions for real time response (flexion (FL), extension (EX), opening (OP) and closure (CL)) from electromyographic (EMG) signals generated from two forearm muscles: palmaris longus and extensor digitorum. The development of the work had two main stages: the low cost hardware for acquisition and conditioning of the EMG analog signals and the processing system for the identification and classification of the movement performed for real time response; the entire system was integrated in a hardware-software application using MATLAB and processing techniques for the discriminant analysis were performed. Three methods were evaluated for pattern recognition getting 98% recognition rates with the method proposed which had the best performance.Keywords: Neural networks and support vector machines, EMG signals, discriminant function, real time response. RESUMEN Este trabajo presenta el diseño e implementación de una nueva técnica para el reconocimiento de cuatro movimientos de la mano (flexión (FL), extensión (EX), apertura (OP) y cierre (CL)) para respuesta en tiempo real a partir de señales electromiográficas EMG generadas desde dos músculos del antebrazo

Feature and muscle selection for an effective hand motion classifier based on electromyography

“…Another study used multilayer perceptron (MLP), SVM and frequency domain to extract the hand motion (HC, HO, WF, and WE). Through the evaluation, the recognition rate (accuracy) was found to be 98% [22], implying that a hybrid combination between physiological parameters and mechanical sensors could improve the performance in pattern recognition. A previous study used a flex sensor for a robotics exoskeleton based on the EMG signal [23].…”

Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements

“…As the effective components of EMG signals are primarily below 100Hz, a butterworth filter was used to eliminate high-frequency components above 100Hz and low-frequency linear drift below 10Hz [51,52]. The preprocessed EMG signal, with both time-domain and frequency-domain representations, is shown in Fig.…”

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals

“…The EMG frequency ranges vary from Hz to 10 kHz, depending on the type of examination (invasive or noninvasive). The most useful and important frequency ranges are within the range from 50 to 150 Hz [ 6 , 7 , 8 , 9 , 10 ].…”

“…The EMG frequency ranges vary from 0.01 Hz to 10 kHz, depending on the type of examination (invasive or noninvasive). The most useful and important frequency ranges are within the range from 50 to 150 Hz [6][7][8][9][10]. It is also important to mention modern solutions in healthcare, which involve measurement of among the others EMG signals-wearable and wireless body-area networks (BAN or WBAN), which integrate multiple sensors for motion, inertial, and biosignals with low-power radios.…”

“…Usually, the BPF with the cut-off frequencies of 20 Hz and 400-600 Hz is used for the demarcation of the physiological frequency band of the EMG signal and removing the both high-and low-frequency interference. Then, the BSF with the cut-off frequencies 49-51 Hz is included in the preprocessing string in order to remove the PLI [8,10,32,33].…”