A High-bandwidth Wireless Wearable Armband Based on Surface Electromyography

Gesture recognition technology based on surface electromyography (sEMG) is essential in the field of human-machine interaction. Wearable armbands that collect sEMG data have gained significant attention from researchers and industries alike. However, existing myoelectric armband systems suffer from deficiencies such as low signal quality and insufficient channels. To tackle this challenge, we propose a 16-channel wireless high-precision armband system characterized by low input-referred noise. This innovative system offers increased channels, a high sampling rate, improved sampling accuracy, and adaptable filter bandwidth. The measurements obtained using this armband for sEMG assessment indicate its potential as a valuable instrument for analyzing muscle electrical activity and facilitating high-bandwidth, non-invasive, and seamless natural interactions between humans and machines.