Abstract
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 ( 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.
Highlights
As many amputees and individuals with impaired motor function have difficulty using traditional user interfaces or assistive and rehabilitative devices, more advanced hands-free human–computer interfaces are desirable
This study examined the performance of myoelectric control as the resolution of analog to digital (A/D) conversion is decreased, and suggested that the resolution of the A/D conversion can be reduced to 8-bit while maintaining or even improving classification performance [18]
For the first aim of this study, the classification performance of the eight multi-feature sets and twenty-six single features was examined for the two sampling rates using an support vector machine (SVM) classifier with
Summary
As many amputees and individuals with impaired motor function have difficulty using traditional user interfaces (e.g., joysticks, mice, and keyboards) or assistive and rehabilitative devices, more advanced hands-free human–computer interfaces are desirable. To create EMG-based human–computer interfaces to be used in an everyday context, they should be simple and non-invasive, such as a watch, an armband, jewelry, or concealed beneath clothing [3]. With the advances in wearable sensors, wireless communication and embedded computing technologies, we can obtain EMG data unintrusively using wearable EMG armbands (for a review, see [4]). These EMG armbands typically include multiple EMG sensors positioned radially around the circumference of a flexible band, allowing ease of donning and wear in daily life. The most widely known EMG armband is the Myo armband by Thalmic Labs, a low-cost consumer-grade EMG device integrating an ARM Cortex-M4 based microcontroller unit, a set of eight dry EMG electrodes, Sensors 2018, 18, 1615; doi:10.3390/s18051615 www.mdpi.com/journal/sensors
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