Muscles Alive Their Functions Revealed by Electromyography, Ed. 4

During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted.

https://www.mdpi.com/1424-8220/17/11/2653/pdf

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review.

Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

Intelligent human computer interaction based on non redundant EMG signal

The principal component analysis method and GRNN neural network are used to construct the gesture recognition system, so as to reduce the redundant information of EMG signals, reduce the signal dimension, improve the recognition efficiency and accuracy, and enhance the feasibility of real-time recognition. Using the means of extracting key information of human motion, the specific action mode is identified. In this paper, nine static gestures are taken as samples, and the surface EMG signal of the arm is collected by the electromyography instrument to extract four kinds of characteristics of the signal. After dimension reduction and neural network learning, the overall recognition rate of the system reached 95.1%, and the average recognition time was 0.19 s.

Surface EMG hand gesture recognition system based on PCA and GRNN

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

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Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

Accurate force control plays a crucial role but still faces many challenges in functional electrical stimulation (FES) based neuroprosthesis applications. This study proposes an surface electromyography (sEMG) driven solution for FES sequential parameter estimation including its amplitude and pulsewidth (PW). The algorithm forms the mapping relationship from voluntary sEMG to the two FES parameters via the correlated muscle forces as an intermediate variable. Six able-bodied subjects are recruited to optimize and validate the proposed method based on the triangular and random grip force. The estimated amplitude and PW are evaluated respectively by stimulating the ipsilateral and contralateral muscle of the sEMG recording points. The accuracy of muscle force reconstruction achieve by the method are investigated by comparing the FES-induced grip force and the originally recorded voluntary grip force (VGF), in terms of the correlation index ( $R$ ). The experimental results reveals excellent performance of the method on inducing functional muscle contraction closely matched the VGF ( $R>0.9$ ), regardless of the estimated amplitudes or PW. Furthermore, the performance differences between the estimated PW and amplitude are not significant ( $p>0.05$ ). These results not only demonstrate the feasibility of the proposed proportional FES control method, but also confirms its practical potential for muscle force control and strengthening in neuroprosthesis applications.

sEMG-Driven Functional Electrical Stimulation Tuning via Muscle Force

Tactile feedback is beneficial to improve the hand prosthesis performance, alleviate phantom pain, reduce muscle fatigue, etc. During the manipulation process, muscle fatigue not only causes discom...

Electrotactile Feedback-Based Muscle Fatigue Alleviation for Hand Manipulation

It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments.

/pdf/a-force-driven-granular-model-for-emg-based-grasp-gy29v4gysa.pdf

A force-driven granular model for EMG based grasp recognition

Surface electromyogram (sEMG)-based hand gesture recognition, which interprets commands given by humans through sEMG signals, performs well in many studies. However, its recognition accuracy drops dramatically due to electrode shift since the distributions of motion classes are changed. Although calibrating the system with newly collected samples after electrode shift maintains the accuracy, collecting labeled samples is inconvenient and time-consuming since the procedure is rigid. However, the calibration may not work properly without label, especially when the change is significant. This study proposes a user friendly and convenient calibration method for hand gesture recognition by an unsupervised domain adaptation method, which only obtains the unlabeled samples of preselected benchmark classes from users in calibration. The change of benchmark classes is captured by unlabeled samples by a clustering method. The other classes are estimated based on the benchmark classes by regression models. As a result, the information of all classes is used to calibrate the system. Linear discriminant analysis is used to demonstrate our model. A dataset with ten subjects is collected to verify the performance empirically. Experimental results confirm that our method utilizes the unlabeled benchmark class samples in calibration and achieves 75.55% average accuracy. Our method is more robust to electrode shift and improves around 8.5% accuracy consistently on all subjects compared with the methods without calibration or label information in calibration. Although the accuracy of our method is slightly less than the ones using label calibration samples, our calibration data collection is more convenient and less complicated.

Kairu Li

Papers

sEMG-Driven Functional Electrical Stimulation Tuning via Muscle Force

Electrotactile Feedback-Based Muscle Fatigue Alleviation for Hand Manipulation

A force-driven granular model for EMG based grasp recognition

Unsupervised Domain Adaptation for Gesture Identification Against Electrode Shift

Finger Disability Recognition Based on Holistically-Nested Edge Detection