Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Abstract

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients' progress in rehabilitation. Despite its widespread Application, existing methods for recording and interpreting EMG data need more signal detection and robust categorization. Recent material science and Artificial Intelligence (AI) developments have significantly improved EMG detection. With an increasingly elderly patient population, HAR is increasingly used to monitor patients' Activities of Daily Living (ADLs) in healthcare settings. It is also being used in security settings to identify suspect behavior, and Surface EMG (sEMG) is a potential non-invasive treatment for HAR since it monitors muscle contractions during exercise. sEMG and AI have revolutionized HAR systems in recent years. Sophisticated methods are required to recognize, break down, manufacture, and classify the EMG signals obtained by muscles. This review summarizes the various research papers based on HAR with EMG. AI has made tremendous contributions to biomedical signals classification. The different approaches of preprocessing, feature extraction, Reduction techniques, Deep Learning (DL) and Machine Learning (ML) based classification methods of EMG signals are then briefly explained. We focused on latest ML/DL methods used in HAR, Hardware involved in HAR with EMG and EMG based Application .We also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

“…In terms of sensor system design, commonly used sensors include inertial sensors (IMUs) [ 3 , 4 , 5 ], plantar pressure sensors [ 6 ], surface electromyography electrodes [ 7 , 8 ], and electroencephalogram electrodes [ 9 ]. IMUs are widely used in gait motion recognition for their high portability, low cost, and environmental friendliness [ 10 , 11 , 12 , 13 , 14 ].…”

Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal

“…Lower limb activity recognition (LLAR) has gained significance due to its practicality in several domains, such as monitoring the health of the elderly, surveillance and security systems, detecting human falls, and numerous more applications [1], [2]. Surface electromyography signals (sEMG), being a kind of neurological signals, are often used in many healthcare applications [3], such as controlling prosthetics or exoskeletons, evaluating neuromuscular diseases, monitoring physical activity, and numerous others [4].…”

Analysis of pelvic floor muscle electromyography parameters in women with or without sexual dysfunction

“…It is relatively small when the muscles are relaxed, but will be elevated when there are more muscle contractions. 5 Pelvic floor muscle (PFM) EMG is a measurement of intravaginal bioelectrical signals collected from a series of muscles of the pelvic floor through contractions and rest according to the Glazer protocol, 6 involving the following phases: pre-baseline rest, phasic contractions, tonic contractions, isometric contractions for muscle endurance evaluation and post-baseline rest. The level of contraction potential represents the degree of myoelectric fatigue, whereas the recovery latency represents the reduced capacity of muscle after contractions.…”