The dexterity of conventional myoelectric prostheses is limited in part by the small datasets used to train the control algorithms. Variations in surface electrode positioning make it difficult to collect consistent data and to estimate motor intent reliably over time. To address these challenges, we developed an inexpensive, easy-to-don sleeve that can record robust and repeatable surface electromyography from 32 embedded monopolar electrodes. Embedded grommets are used to consistently align the sleeve with natural skin markings (e.g., moles, freckles, scars). The sleeve can be manufactured in a few hours for less than $60. Data from seven intact participants show the sleeve provides a signal-to-noise ratio of 14, a don-time under 11 seconds, and sub-centimeter precision for electrode placement. Furthermore, in a case study with one intact participant, we use the sleeve to demonstrate that neural networks can provide simultaneous and proportional control of six degrees of freedom, even 263 days after initial algorithm training. We also highlight that consistent recordings, accumulated over time to establish a large dataset, significantly improve dexterity. These results suggest that deep learning with a 74-layer neural network can substantially improve the dexterity and stability of myoelectric prosthetic control, and that deep-learning techniques can be readily instantiated and further validated through inexpensive sleeves/sockets with consistent recording locations.
Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
Human-Robot Interfaces and Interaction Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy（人机交互实验室，意大利技术研究院，热那亚，意大利）；Dept. of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy（信息学、生物工程、机器人与系统工程系，热那亚大学，热那亚，意大利）；Airworks SRL, Povoletto, Italy（Airworks SRL，意大利）
Natural muscles provide mobility in response to nerve impulses. Electromyography (EMG) measures the electrical activity of muscles in response to a nerve's stimulation. In the past few decades, EMG signals have been used extensively in the identification of user intention to potentially control assistive devices such as smart wheelchairs, exoskeletons, and prosthetic devices. In the design of conventional assistive devices, developers optimize multiple subsystems independently. Feature extraction and feature description are essential subsystems of this approach. Therefore, researchers proposed…
University of Utah（犹他大学）；Department of Neurosurgery, University of Utah（犹他大学神经外科）
Bypass sockets allow researchers to perform tests of prosthetic systems from the prosthetic user's perspective. We designed a modular upper-limb bypass socket with 3D-printed components that can be easily modified for use with a variety of terminal devices. Our bypass socket preserves access to forearm musculature and the hand, which are necessary for surface electromyography and to provide substituted sensory feedback. Our bypass socket allows a sufficient range of motion to complete tasks in the frontal working area, as measured on non-amputee participants. We examined the performance of non…
In this paper, we present our work on developing robot arm prosthetic via deep learning. Our work proposes to use transfer learning techniques applied to the Google Inception model to retrain the final layer for surface electromyography (sEMG) classification. Data have been collected using the Thalmic Labs Myo Armband and used to generate graph images comprised of 8 subplots per image containing sEMG data captured from 40 data points per sensor, corresponding to the array of 8 sEMG sensors in the armband. Data captured were then classified into four categories (Fist, Thumbs Up, Open Hand, Rest…
Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal sensor data may include various environment sensors including vision, a…
Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）
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））
A Continuous sEMG-Based Prosthetic Hand Control System Without Motion or Force Sensors
School of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）；Henan Key Laboratory of Brain Science and Brain Computer Interface Technology, Zhengzhou University（郑州大学脑科学与脑机接口技术重点实验室）；Engineering Department of International College, Zhengzhou University（郑州大学国际学院工程系）；School of Cyber Science and Engineering, Zhengzhou University（郑州大学网络安全科学与工程学院）；Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University（上海脑机智能信息行为重点实验室，上海国际关系大学）；Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China（成都脑科学研究院临床医院，教育部神经信息关键实验室，电子科技大学）；Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China（神经信息研究单位，中国医学科学院，成都，中国）
ReactEMG: Stable, Low-Latency Intent Detection from sEMG via Masked Modeling
Department of Mechanical Engineering, Columbia University in the City of New York（机械工程系，哥伦比亚大学纽约市分校）；Department of Computer Science, Columbia University in the City of New York（计算机科学系，哥伦比亚大学纽约市分校）；Department of Rehabilitation and Regenerative Medicine, Columbia University Irving Medical Center（康复与再生医学系，哥伦比亚大学伊万杰琳医学中心）
George Mason University（乔治梅森大学）；Center for Adaptive Systems of Brain-Body Interactions（脑-体交互自适应系统中心）；Infinite Biomedical Technologies（无限生物医学技术公司）；MedStar National Rehabilitation Hospital（MedStar国家康复医院）
Technological advances in multi-articulated prosthetic hands have outpaced the methods available to amputees to intuitively control these devices. Amputees often cite difficulty of use as a key contributing factor for abandoning their prosthesis, creating a pressing need for improved control technology. A major challenge of traditional myoelectric control strategies using surface electromyography electrodes has been the difficulty in achieving intuitive and robust proportional control of multiple degrees of freedom. In this paper, we describe a new control method, proprioceptive sonomyographic…
