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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&#x2019; 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&#x2019; 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.

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