Conditioning and Sampling Issues of EMG Signals in Motion Recognition of Multifunctional Myoelectric Prostheses

Abstract

Historically, the investigations of electromyography (EMG) pattern recognition-based classification of intentional movements for control of multifunctional prostheses have adopted the filter cut-off frequency and sampling rate that are commonly used in EMG research fields. In practical implementation of a multifunctional prosthesis control, it is desired to have a higher high-pass cut-off frequency to reduce more motion artifacts and to use a lower sampling rate to save the data processing time and memory of the prosthesis controller. However, it remains unclear whether a high high-pass cut-off frequency and a low-sampling rate still preserve sufficient neural control information for accurate classification of movements. In this study, we investigated the effects of high-pass cut-off frequency and sampling rate on accuracy in identifying 11 classes of arm and hand movements in both able-bodied subjects and arm amputees. Compared to a 5-Hz high-pass cut-off frequency, excluding the EMG components below 60 Hz decreased the average accuracy of 0.1% in classifying the 11 movements across able-bodied subjects and increased the average accuracy of 0.1 and 0.4% among the transradial (TR) and shoulder disarticulation (SD) amputees, respectively. Using a 500 Hz instead of a 1-kHz sampling rate, the average classification accuracy only dropped about 2.0% in arm amputees. The combination of sampling rate and high-pass cut-off frequency of 500 and 60 Hz only resulted in about 2.3% decrease in average accuracy for TR amputees and 0.4% decrease for SD amputees in comparison to the generally used values of 1 kHz and 5 Hz. These results suggest that the combination of sampling rate of 500 Hz and high-pass cut-off frequency of 60 Hz should be an optimal selection in EMG recordings for recognition of different arm movements without sacrificing too much of classification accuracy which can also remove most of motion artifacts and power-line interferences for improving the performance of myoelectric prosthesis control.

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

“…They used two channels and performed experiments in real-time. It is worth mentioning the study of Li et al (2011), which recognizes four different grasps among eleven gestures with 71.3% of accuracy, validated on amputees. However, their work included high-density sEMG signals (twelve channels).…”

“…Some studies with amputees using few number of electrodes have been conducted in order to fulfil this gap, such as done in Al-Timemy et al (2013), Cipriani et al (2011), Li et al (2011, Kumar et al (2013) and Tenore et al (2009). In particular, in Kumar et al (2013) a method based on wavelet maxima density was proposed as a non-linear parameter to extract relevant from sEMG signals using only one channel, but no grasp gestures were considered.…”

“…In particular, in Kumar et al (2013) a method based on wavelet maxima density was proposed as a non-linear parameter to extract relevant from sEMG signals using only one channel, but no grasp gestures were considered. Grasp gestures were did consider in Li et al (2011), but using high-density sEMG signals (twelve electrodes). Nevertheless, in all these previous works conducted with amputees, a common lack of experiments with hand, wrist and individual finger gestures can be noticed, and few of these works have even included dexterous movements, and only for high-density.…”

Electromyography Pattern-Recognition-Based Control of Powered Multifunctional Upper-Limb Prostheses

“…Therefore, a higher high-pass cut-off frequency will be expected to significantly reduce more motion artifacts in the captured EMG signals; this may enhance the control accuracy and stability of a myoelectric prosthesis. The results from our recent study (Li et al, 2011) showed that the accuracy for the classification of a number of classes of arm movements could not benefit much from acquiring more low frequency components of EMG signals. Including 20-100 Hz frequency band components of EMG signals only slightly increased the classification accuracy in both of able-bodied subjects (about 0.25%) and amputees (about 1.6%).…”

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

“…Unfortunately, there are not any filters that can remove one hundred percent of noise. Sometimes it removes some important parts of real EMG signals (De Luca, Gilmore, Kuznetsov, & Roy, 2010;Li, Li, Yu, & Geng, 2011). So EMG features that have high tolerance for biological and environmental noises should increase the ability of EMG-based gesture recognition.…”