A Numerical Comparison of Deeply Quantized Models for sEMG Hand Gesture Classification on Constrained Devices

In the evolving landscape of healthcare, Machine Learning (ML) and Deep Learning (DL) have revolutionized medical practises, particularly in Smart Health. This paper explores their application in Intelligent Cyber Physical Systems (iCPSs), focusing on surface Electromyography (sEMG) signal classification for prosthetic control. We conduct a detailed quan-titative study, comparing the performances and memory efficiency of Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells and Convolutional Neural Networks (CNNs) with Temporal Convolutional Networks (TCNs) layers. Using the most popular quantization frameworks, TensorFlow Lite (TFLite) and Quantized Keras (QKeras), we quantify the impact on the proposed models of both INT8 Quantization and Deep Quantization. The aim is to undertake a comparison among the proposed structures and prove that their Deeply Quantized version lead to minimal performance degradation related to memory footprint's substantial reduction. Our contributions include a custom TCN layer, compliant with QKeras deep quantization, a comparative analysis between LSTM and TCN and evaluation of quantization frameworks. We offer insights into model performance and efficiency, informing their potential in real-world medical contexts.