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

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, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Hand Pattern Recognition Using Smart Band

The Importance of gesture recognition has widely spread around the world. Many research strategies have been proposed to study and recognize gestures, especially facial and hand gestures. Distinguishing and recognizing hand gestures is vital in hotspot fields such as bionic parts, powered exoskeleton, diagnosing muscle disorders, etc. Recognizing such gesture patterns can also create a stress-free and fancy user interface for mobile phones, gaming consoles and other such devices. The objective is to design a simple yet efficient wearable hand gesture recognizing system. This thesis also shows that by taking both EMG and accelerometer data into account, can improve the system to recognize more patterns with higher accuracy levels. For this, a hand band embedded with a triple axis accelerometer and three surface EMG electrodes is employed to source the system. The non-invasive surface EMG electrodes senses muscle action while the accelerometer senses the hand motions. The EMG signal is passed through analog front-end module for noise filtering and signal amplification. An ARM Cortex processor converts the analog EMG and accelerometer signal into digital and transmits to a PC via Bluetooth protocol. On the receiver section, the raw EMG and acceleration data is further processed and decomposed offline using MATLAB tools to extract features such as root mean square, waveform length, threshold crossing, variance and mean. Extracted features are then fed through multi-class SVM (Support Vector Machine) process for pattern recognition. The chapters below discuss in greater detail on pattern recognition technique and other modules involved

Using Photoplethysmography for Simple Hand Gesture Recognition

A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications

From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models

Multimodal hand gesture recognition (HGR) systems can achieve higher recognition accuracy. However, acquiring multimodal gesture recognition data typically requires users to wear additional sensors, thereby increasing hardware costs. This paper proposes a novel generative approach to improve Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial Measurement Unit (IMU) signals. Specifically, we trained a deep generative model based on the intrinsic correlation between forearm sEMG signals and forearm IMU signals to generate virtual forearm IMU signals from the input forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU signals were fed into a multimodal Convolutional Neural Network (CNN) model for gesture recognition. To evaluate the performance of the proposed approach, we conducted experiments on 6 databases, including 5 publicly available databases and our collected database comprising 28 subjects performing 38 gestures, containing both sEMG and IMU data. The results show that our proposed approach outperforms the sEMG-based unimodal HGR method (with increases of 2.15%-13.10%). It demonstrates that incorporating virtual IMU signals, generated by deep generative models, can significantly enhance the accuracy of sEMG-based HGR. The proposed approach represents a successful attempt to transition from unimodal HGR to multimodal HGR without additional sensor hardware

A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems