Impact of Sampling Rate and Quantization on EMG-Based HMI: Achieving Hardware Invariance by Advanced Feature Engineering
Electromyography (EMG) serves as a robust signal source for developing intuitive human-machine interfaces (HMIs). With the proliferation of commercial and prototype EMG armbands, there is a growing need to balance high-accuracy gesture recognition with hardware constraints. While deep learning architectures have achieved remarkable performance, they often require significant computational resources and high-end sensors. This study investigates an algorithmic-centric perspective, exploring whether sophisticated feature extraction (FE) can compensate for reduced hardware specifications. Using a 20-gesture dataset collected from 10 subjects, we evaluated performance across three hardware configurations: 500 Hz/8-bit (C1), 500 Hz/12-bit (C2), and 1000 Hz/8-bit (C3). We compared traditional FE methods against modern paradigms designed to capture spatial and spatio-temporal signal dynamics, specifically PHASOR, Myoelectric Temporal Patching (MTP), and the proposed WaveLSTM (Wave long short-term memory) framework. WaveLSTM embeds deep-learning-inspired processing schemes, i.e., utilizing short- and long-term memory components, into a computationally efficient pipeline with significantly lower overhead than standard deep learning models. Our results demonstrate that WaveLSTM achieves superior accuracy, exceeding 95% in a leave-one-trial-out validation scheme. Notably, while classical FE approaches exhibited lower overall recognition rates, WaveLSTM demonstrated hardware invariance, maintaining high performance regardless of changes in sampling frequency (p=0.275) or ADC resolution (p=0.941). These findings suggest that advanced spatio-temporal feature engineering can enable high-performance, complex gesture recognition on low-cost, low-power wearable devices without sacrificing accuracy.