Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks

Min, Huasong; Chen, Ziming; Fang, Bin; Xia, Ziwei; Song, Yong; Wang, Zongtao; Zhou, Quan; Sun, Fuchun

doi:10.1155/2021/6680417

Cited by 5 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The composition of human hand muscles is basically the same, but the degree of development of muscle tissue varies between different subjects [30], so the collected EMG signals will also be different, making the control performance of current prosthetic devices less than ideal. Min et al proposed a cross-individual gesture recognition algorithm based on a long short-term memory network, conducted a classification study of four kinds of actions on four healthy subjects, and finally achieved a classification accuracy of 86.5% [31]. Liao et al carried out gesture recognition among multi-object groups, combined features with the KNN classification algorithm, and the accuracy of six gesture classifications reached 91.05% [32].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

et al. 2023

View full text Add to dashboard Cite

Objective. Prosthetic systems are used to improve the quality of life of post-amputation patients, and research on surface electromyography (sEMG)-based gesture classification has yielded rich results. Nonetheless, current gesture classification algorithms focus on the same subject, and cross-individual classification studies that overcome physiological factors are relatively scarce, resulting in a high abandonment rate for clinical prosthetic systems. The purpose of this research is to propose an algorithm that can significantly improve the accuracy of gesture classification across individuals. Approach. Eight healthy adults were recruited, and surface electromyography data of seven daily gestures were recorded. A modified Fuzzy Granularized Logistic Regression (FG_LogR) algorithm is proposed for cross-individual gesture classification. Main results. The results show that the average classification accuracy of the four features based on the FG_LogR algorithm is 79.7%, 83.6%, 79.0%, and 86.1%, while the classification accuracy based on the Logistic Regression (LogR) algorithm is 76.2%, 79.5%, 71.1%, and 81.3%, the overall accuracy improved ranging from 3.5% to 7.9%. The performance of the FG_LogR algorithm is also superior to the other five classic algorithms, and the average prediction accuracy has increased by more than 5%. Conclusion. The proposed FG_LogR algorithm improves the accuracy of cross-individual gesture recognition by fuzzy and granulating the features, and has the potential for clinical application. Significance. The proposed algorithm in this study is expected to be combined with other feature optimization methods to achieve more precise and intelligent prosthetic control and solve the problems of poor gesture recognition and high abandonment rate of prosthetic systems.

show abstract

Section: Introductionmentioning

confidence: 99%

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Among them: Since sEMG has the characteristics of non-invasiveness, convenience, freedom of space constraints, and close correlation with forearm muscle contraction, researchers have conducted extensive exploration on sEMG-based gesture recognition [6]- [7]. According to recent research reports, the data extracted from sEMG has been used in transfer learning [8], continuous gesture decoding [9], cross individual gesture recognition [10], fingertip and grip force estimation [11]- [13], and elbow force prediction [14]- [15], which obtained desirable recognition accuracy.…”

Section: Introduction He Human-machine Interface (Hmi) Based On Gestu...mentioning

confidence: 99%

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

Qing

Liu

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not been effectively combined. There are limitations in applications such as prosthetic control and clinical rehabilitation, etc. In this paper, a grasping gesture and force recognition strategy based on wearable A-mode ultrasound and two-stage cascade model is proposed, which can simultaneously estimate the force while classifying the grasping gesture. This paper experiments five grasping gestures and four force levels (5-50%MVC). The results demonstrate that the performance of the proposed model is significantly better than that of the traditional model both in classification and regression (p<0.001). Additionally, the two-stage cascade regression model (TSCRM) used the Gaussian Process regression model (GPR) with the mean and standard deviation (MSD) feature obtains excellent results, with normalized root-mean-square error (nRMSE) and correlation coefficient (CC) of 0.1049 ± 0.0374 and 0.9461±0.0354, respectively. Besides, the latency of the model meets the requirement of real-time recognition (T<15ms). Therefore, the research outcomes prove the feasibility of the proposed recognition strategy and provide a reference for the field of prosthetic control, etc.

show abstract

“…The representative papers in recent years are as follows: Min et al [18] proposed a cross individual gesture decoding method based on Long Short-Term Memory network (LSTM)-Cross individual dual network structure (CI-LSTM) in 2021. Compared with other algorithm models, the decoding accuracy of the model was improved by 9.15% on average.…”

Section: Introductionmentioning

confidence: 99%

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

Qing

Cai

et al. 2021

Sensors

View full text Add to dashboard Cite

The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.

show abstract

Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks

Cited by 5 publications

References 24 publications

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

Contact Info

Product

Resources

About