Intent based recognition of walking and ramp activities for amputee using sEMG based lower limb prostheses

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals

Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunction, and electrode sheet detachment. These non-ideal factors severely compromise the accuracy of continuous motion recognition and the reliability of clinical applications. To tackle this challenge, this paper develops a multi-task parallel learning framework for continuous motion estimation with incomplete sEMG signals. Concretely, a residual network is incorporated into a recurrent neural network to integrate the information flow of hidden states and reconstruct random and consecutive missing sEMG signals. The attention mechanism is applied for redistributing the distribution of weights. A jointly optimized loss function is devised to enable training the model for simultaneously dealing with signal anomalies/absences and multi-joint continuous motion estimation. The proposed model is implemented for estimating hip, knee, and ankle joint angles of physically competent individuals and patients during diverse exercises. Experimental results indicate that the estimation root-mean-square errors with 60% missing sEMG signals steadily converges to below 5 degrees. Even with multi-channel electrode sheet shedding, our model still demonstrates cutting-edge estimation performance, errors only marginally increase 1 degree.

A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.

sEMG-Based End-to-End Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model

Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots. The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R2, and (CC) values of the model are 3.79°, 0.96, and 0.98, respectively, which are better than traditional deep learning models such as Informer (4.14, 0.95, 0.98), CNN (5.56, 0.89, 0.96) and CNN-BiLSTM (3.97, 0.95, 0.98). In addition, the prediction time of our proposed model is only 11.67 ± 0.67 ms, which is less than 100 ms. Therefore, the real-time and accuracy of the model can meet the continuous prediction of human knee joint angle in practice.

Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units

Human action intent recognition has become increasingly dependent on computational accuracy, real-time responsiveness, and model lightness. Model selection, data filtering, and experimental design are three critical factors for the recognition of human intention in research. However, the performance of machine learning algorithms can vary depending on factors such as sensor location, the number of sensors used, channel selection, and dimensional combinations. Moreover, the collection of adequate and balanced data in such scenarios can be challenging. To address this issue, we present a comparative analysis of 12 commonly used machine learning algorithms for human action intention recognition. The synthetic minority oversampling technique is applied to fill in missing data. Traversing all possible combinations would require conducting 686 experiments, which is a daunting task in terms of both cost and efficiency. To tackle this challenge, we employ an orthogonal experiment design based on the Quasi-horizontal method. Our analysis indicates that lightGBM outperforms other algorithms in recognizing eight human daily activities. Furthermore, we conduct a polar difference and variance analysis based on a comprehensive balanced multi-metric orthogonal experiment for lightGBM using various sensor combinations and dimensions. The optimal combinations of different sensor numbers in terms of position, channel, and dimension are derived using this approach. Notably, our experimental design reduces the number of experiments required to only 49 times.

Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development

Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson's correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (rposition = 0.9099 and rmoment = 0.9707) whereas, PL (rposition = 0.9001, rmoment = 0.9703) and GMax+VM (rposition = 0.9010, rmoment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.

A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge - the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user. In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: high-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Artificial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. The visible effects of AI can be observed in key competencies and corporate processes such as knowledge management, as well as consumer outcomes including service quality perceptions and satisfaction. This study aims to optimize the human resource management (HRM) process, reduce the workload of human resource managers, and improve work efficiency. Based on AI digitization technology, a salary prediction model (SPM) is designed using a backpropagation neural network (BPNN), and the Nesterov and Adaptive Moment Estimation (Nadam) algorithms are integrated to optimize the model. Next, the content information of the resumes are used to predict the hiring salary of the candidates and validate the model. Results show that compared with other optimization algorithms, the final predicted result score of the Nadam optimization algorithm is 0.75%, and the training period is 186 s, providing the best optimization effect and the fastest convergence speed. Moreover, the BPNN-based SPM optimized by Nadam has good performance in the learning process and the accuracy rate can reach 79.4%, which verifies the validity of the SPM. The outcomes of this study can provide a reference for HRM systems based on data mining technology.

Adaptive Lower Limb Pattern Recognition for Multi-Day Control

Pattern recognition in EMG-based control systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. To circumvent concept drift, adaptation strategies could be used to retrain a pattern recognition system, which could lead to comparable error rates over multiple days. In this study, we investigated the error rate development over one week and compared three adaptation strategies to reduce the error rate increase. The three adaptation strategies were based on entropy, on backward prediction and a combination of backward prediction and entropy. Ten able-bodied subjects were measured on four measurement days while performing gait-related activities. During the measurement electromyography and kinematics were recorded. The three adaptation strategies were implemented and compared against the baseline error rate and against adaptation using the ground truth labels. It can be concluded that without adaptation the baseline error rate increases significantly from day 1 to 2, but plateaus on day 2, 3 and 7. Of the three tested adaptation strategies, entropy based adaptation showed the smallest increase in error rate over time. It can be concluded that entropy based adaptation is simple to implement and can be considered a feasible adaptation strategy for lower limb pattern recognition.

The Impact of Mixed-Ownership Reform on Innovation Performance of Manufacturing Companies: Evidence from China

Exploring the impact of a new round of mixed-ownership reform on enterprise development has far-reaching implications for the future development of mixed-ownership reform and the improvement of internal and external corporate governance of the enterprise. This study makes a comprehensive analysis of the relationship between mixed-ownership reform and innovation performance using a combination of empirical testing and theoretical analysis. In terms of theoretical analysis, this study summarizes research on mixed-ownership reform and innovation performance, while also integrating the theoretical basis of the development and institutional background of China's current mixed-ownership reform, and investigating the impact of mixed-ownership reform on innovation performance. A-share list of manufacturing companies from 2015 to 2018 is selected as the research object, and K-nearest neighbor matching, multiple regression model, and propensity score matching are used to test the effect of mixed-ownership reform on innovation performance. By comparing the changes in the innovation performance level of state-owned enterprises (SOEs) before and after the mixed-ownership reform, this study proposes that the SOEs will not only enhance the competitiveness among manufacturing companies but also will have a positive impact on the high-quality development of the entire society and industrial transformation and upgrading.

Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

Objective

In order to study the motion recognition intention of lower limb prosthesis based on the CNN deep learning algorithm.

Methods

A convolutional neural network (CNN) model was established to reconstruct the motion pattern. Before the movement mode of the affected side was converted, the sensor was bound to the healthy side. The classifier was employed to extract and classify the features, so as to realize the accurate description of the movement intention of the disabled.

Results

The method proposed in this research can achieve 98.2% recognition rate of the movement intention of patients with lower limb amputation under different terrains, and the recognition rate can reach 97% after the pattern converted between the five modes was added.

Conclusion

The deep learning algorithm that automatically recognized and extracted features can effectively improve the control performance on the intelligent lower limb prosthesis and realize the natural and seamless conversion of the intelligent prosthesis in a variety of motion modes.

Artificial Intelligence Auxiliary Algorithm for Wushu Routine Competition Decision Based on Feature Fusion

The developments of modern science and technology have significantly promoted the progress of sports science. Advanced technological methods have been widely used in sports training, which has not only improved the scientific level of training but also promoted the continuous growth of sports technology and competition results. Competitive Wushu routine is an important part of Chinese Wushu. The development trend of competitive Wushu routine affects the development of the whole Wushu movement. To improve the training effect of the Wushu routine using artificial intelligence, this paper employed fuzzy information processing and feature extraction technology to analyze the visual features in the process of Wushu competition. The deep neural network-based region segmentation method was employed for implicit feature extraction to examine the shape, texture, and other image features of Wushu routines and improve the recognition performance. The proposed feature extraction model achieved the highest average accuracy of 93.98% accuracy as compared to other contemporary algorithms. Finally, the model was evaluated to validate the superior performance of the proposed method in improving the decision-making ability and effective instruction ability of the martial arts routine competition.

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions

Objective.Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations.Approach.We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses.Main results.This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives.Significance.This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.