User-Tailored Hand Gesture Recognition System for Wearable Prosthesis and Armband Based on Surface Electromyogram

Meng, Long; Jiang, Xinyu; Liu, Xiangyu; Fan, Jiahao; Ren, Haoran; Guo, Yao; Diao, Haikang; Wang, Zihao; Chen, Chen; Dai, Chenyun; Chen, Wei

doi:10.1109/tim.2022.3217868

Cited by 18 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the use of HD electrodes combined with the spatiotemporal feature extraction ability of DNN is often used to solve the above problems. Meng et al constructed 1D-CNN, 2D-CNN, and CNN-LSTM to carry out the classification task among users on the 256-channel sEMG datasets of 10 gestures ( Meng et al, 2022 ). Compared with SVM, the recognition accuracies of three networks increased significantly.…”

Section: Discussionmentioning

confidence: 99%

Feasibility study on the application of a spiking neural network in myoelectric control systems

Sun

Chen

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage–current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1–2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Feasibility study on the application of a spiking neural network in myoelectric control systems

Sun

Chen

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Thus, feature extraction and classification are among the most critical stages in sEMG control systems. Classification techniques based on sEMG have been extensively studied [ 14 ], and sEMG feature extraction approaches generally focus on time-domain features, frequency-domain features, or time–frequency mixed features. Time-domain features are intuitive and can clearly reflect the changes of sEMG signals over time.…”

Section: Experimental Methodsmentioning

confidence: 99%

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Zhang

Hong

Zhang

et al. 2023

Sensors

View full text Add to dashboard Cite

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person’s intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1–20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time–frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

show abstract

“…Based on their functionalities, data collection devices can be categorized into wearable devices, visual devices, and radar devices. Wearable devices require individuals to wear specific equipment on their bodies to collect signals, such as Surface Electromyogram (sEMG) [5]. While they can accurately capture gesture signals, wearable devices require continuous wearing throughout the measurement process, and the wearing process itself is complex.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning

Shi,

Qiao,

Shu

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network’s robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets.

show abstract

User-Tailored Hand Gesture Recognition System for Wearable Prosthesis and Armband Based on Surface Electromyogram

Cited by 18 publications

References 55 publications

Feasibility study on the application of a spiking neural network in myoelectric control systems

Feasibility study on the application of a spiking neural network in myoelectric control systems

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning

Contact Info

Product

Resources

About