supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination

The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.

MEET: Mixture of Experts Extra Tree-Based sEMG Hand Gesture Identification

Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand gesture recognition is a subject of study that has recently emerged as a research interest in robotic hand control using electromyography (EMG). Surface electromyography (sEMG) is a primary technique used in EMG, which is popular due to its non-invasive nature and is used to capture gesture movements using signal acquisition devices placed on the surface of the forearm. Moreover, these signals are pre-processed to extract significant handcrafted features through time and frequency domain analysis. These are helpful and act as input to machine learning (ML) models to identify hand gestures. However, handling multiple classes and biases are major limitations that can affect the performance of an ML model. Therefore, to address this issue, a new mixture of experts extra tree (MEET) model is proposed to identify more accurate and effective hand gesture movements. This model combines individual ML models referred to as experts, each focusing on a minimal class of two. Moreover, a fully trained model known as the gate is employed to weigh the output of individual expert models. This amalgamation of the expert models with the gate model is known as a mixture of experts extra tree (MEET) model. In this study, four subjects with six hand gesture movements have been considered and their identification is evaluated among eleven models, including the MEET classifier. Results elucidate that the MEET classifier performed best among other algorithms and identified hand gesture movement accurately.