In this paper, we present a putEMG dataset intended for evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches, and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject's forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. putEMG dataset is available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at: https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. Accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin's and Du's feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. Presented dataset can be used as a benchmark for various classification methods, evaluation of electrode localisation concepts, or development of classification methods invariant to user-specific features or electrode displacement.
Airwriting Recognition is the task of identifying letters written in free space with finger movement. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and user-friendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is there…
By using a computer keyboard as a finger recording device, we construct the largest existing dataset for gesture recognition via surface electromyography (sEMG), and use deep learning to achieve over 90% character-level accuracy on reconstructing typed text entirely from measured muscle potentials. We prioritize the temporal structure of the EMG signal instead of the spatial structure of the electrode layout, using network architectures inspired by those used for real-time spoken language transcription. Our architecture recognizes the rapid movements of natural computer typing, which occur at …
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
University of California Los Angeles (UCLA)（加州大学洛杉矶分校（UCLA））；New York University (NYU)（纽约大学（NYU））；NYU WIRELESS（纽约大学无线研究中心）；NYU Center for Urban Science and Progress (CUSP)（纽约大学城市科学与进步中心（CUSP））
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first convert…
Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.
University of California, Berkeley（加州大学伯克利分校）；University of Bologna（博洛尼亚大学）；ETH Zurich（苏黎世联邦理工学院）
Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition. Some use cases require the classifier to be robust against varying force changes, while others demand to distinguish between different effort levels of performing the same gesture. We use brain-inspired hyperdimensional computing paradigm to build classification models that are both robust to these variations and able to recognize multiple contraction levels. Experimental results on 5 subjects performing 9 gestures with 3 effort levels show up to 39.17% accuracy drop when training and testin…
Institute of Neuroinformatics（神经信息学研究所）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；University of Modena e Reggio Emilia（摩德纳-雷焦艾米利亚大学）；University of Bologna（博洛尼亚大学）
Discrimination of hand gestures based on the decoding of surface electromyography (sEMG) signals is a well-establish approach for controlling prosthetic devices and for Human-Machine Interfaces (HMI). However, despite the promising results achieved by this approach in well-controlled experimental conditions, its deployment in long-term real-world application scenarios is still hindered by several challenges. One of the most critical challenges is maintaining high EMG data classification performance across multiple days without retraining the decoding system. The drop in performance is mostly d…
Airwriting Recognition refers to the problem of identification of letters written in space with movement of the finger. It can be seen as a special case of dynamic gesture recognition wherein the set of gestures are letters in a particular language. Surface Electromyography (sEMG) is a non-invasive approach used to capture electrical signals generated as a result of contraction and relaxation of the muscles. sEMG has been widely adopted for gesture recognition applications. Unlike static gestures, dynamic gestures are user-friendly and can be used as a method for input with applications in Hum…
Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focused mainly on medical applications, there has been growing interest in utilizing sEMG as a sensing modality to enable next-generation, high-bandwidth, and natural human-machine interfaces. In the first …
Bias in Surface Electromyography Features across a Demographically Diverse Cohort
Graduate Group in Computer Science, University of California, Davis（计算机科学研究生组，加州大学戴维斯分校）；Department of Electrical and Computer Engineering, University of California, Davis（电气与计算机工程系，加州大学戴维斯分校）；Department of Mechanical and Mechatronic Engineering and Advanced Manufacturing, California State University, Chico（机械与机电工程及先进制造系，加州州立大学奇科分校）；Department of Neurobiology, Physiology & Behavior, University of California, Davis（神经生物学、生理学与行为系，加州大学戴维斯分校）；Department of Otolaryngology / Head & Neck Surgery, University of California, Davis（耳鼻喉科/头颈外科系，加州大学戴维斯分校）；Center for Mind & Brain, University of California, Davis（心智与大脑中心，加州大学戴维斯分校）；Department of Mechanical & Aerospace Engineering, University of California, Davis（机械与航空航天工程系，加州大学戴维斯分校）
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
Khoury College of Computer Sciences, Northeastern University（东北大学卡内基计算机科学学院）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Department of Physical Therapy, Movement, and Rehabilitation Sciences, Northeastern University（东北大学物理治疗、运动与康复科学系）
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Con…
Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to …
CERN（欧洲核子研究中心）；German Aerospace Center (DLR)（德国航空航天中心）；Department of Industrial Engineering, DII, University of Trento（特伦托大学工业工程系）
This work has been conducted in the context of pattern-recognition-based control for electromyographic prostheses. It presents a k-nearest neighbour (kNN) classification technique for gesture recognition, extended by a proportionality scheme. The methods proposed are practically implemented and validated. Datasets are captured by means of a state-of-the-art 8-channel electromyography (EMG) armband positioned on the forearm. Based on this data, the influence of kNN's parameters is analyzed in pilot experiments. Moreover, the effect of proportionality scaling and rest thresholding schemes is inv…
Artificial Intelligence Thrust, HKUST (Guangzhou)（香港科技大学（广州）人工智能方向）；School of Computing and Information Technology, Great Bay University（湾区大学计算机与信息技术学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Department of Computer Science and Engineering, HKUST（香港科技大学计算机科学与工程系）；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）
SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition
School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University（电子设计自动化技术创新中心，杭州电子科技大学）
A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
