An electromyography hardware acquisition unit was designed using commodity components with a view towards future full-scale ASIC integration. Surface electromyography was accomplished at forearm muscle sites using both dry and Ag/AG-CL differential electrodes. The acquired signal was amplified differentially and then processed using a combination of a bandpass filter and a dual notch filter. The filters were implemented using eighth-order switch capacitor filters with a reference clock frequency of 20 kHz. The bandpass filters retained the salient electromyographic frequencies of 20 Hz to 120 Hz. The notch filters were placed at the problematic 60 Hz and 120 Hz powerline interference frequencies. The processed analog signals were digitized using an eight bit analog-to-digital converter and then further processed for frequency content within a microcontroller. Bluetooth was used to transfer data from the microcontroller to a variety of Bluetooth enabled hardware devices including smart phones and laptops. Gesture recognition software implemented using support vector machines interpreted both the muscle activity intensity and the sequencing of muscle activation thereby providing gesture recognition. The system was designed to accommodate up to six sensors but the number of sensors can be increased by multiplexing inputs. Due to the inherent train-ability of support vector machines, the implemented gesture recognition algorithm may be trained for various applications as well as for different users of the implemented system.
