Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

We used an armband with embedded surface electromyography (sEMG) electrodes, together with machine-learning (ML) models, to automatically detect lifting-lowering activities and classify hand loads. Nine healthy participants (4 male and 5 female) completed simulated lifting-lowering tasks in various conditions and with two different hand loads (2.3 and 6.8 kg). We compared three sEMG signal feature sets (i.e., time, frequency, and a combination of both domains) and three ML classifiers (i.e., Random Forest, Support Vector Machine, and Logistic Regression). Both Random Forest and Support Vector Machine models, using either time-domain or time- and frequency-domain features, yielded the best performance in detecting lifts, with respective accuracies of 79.2% (start) and 86.7% (end). Similarly, both ML models yielded the highest accuracy (80.9%) in classifying the two hand loads, regardless of the sEMG features used, emphasizing the potential of sEMG armbands for assessing exposure and risks in occupational lifting tasks.

Knee Angle Estimation from Surface EMG during Walking Using Attention-Based Deep Recurrent Neural Networks: Feasibility and Initial Demonstration in Cerebral Palsy

Accurately estimating knee joint angle during walking from surface electromyography (sEMG) signals can enable more natural control of wearable robotics like exoskeletons. However, challenges exist due to variability across individuals and sessions. This study evaluates an attention-based deep recurrent neural network combining gated recurrent units (GRUs) and an attention mechanism (AM) for knee angle estimation. Three experiments were conducted. First, the GRU-AM model was tested on four healthy adolescents, demonstrating improved estimation compared to GRU alone. A sensitivity analysis revealed that the key contributing muscles were the knee flexor and extensors, highlighting the ability of the AM to focus on the most salient inputs. Second, transfer learning was shown by pretraining the model on an open source dataset before additional training and testing on the four adolescents. Third, the model was progressively adapted over three sessions for one child with cerebral palsy (CP). The GRU-AM model demonstrated robust knee angle estimation across participants with healthy participants (mean RMSE 7 degrees) and participants with CP (RMSE 37 degrees). Further, estimation accuracy improved by 14 degrees on average across successive sessions of walking in the child with CP. These results demonstrate the feasibility of using attention-based deep networks for joint angle estimation in adolescents and clinical populations and support their further development for deployment in wearable robotics.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression

In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human-robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R² of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer's motion intentions in human-robot collaboration control.

Lower body kinematics estimation during walking using an accelerometer

Measuring and predicting accurate joint angles are important to developing analytical tools to gauge users' progress. Such measurement is usually performed in laboratory settings, which is difficult and expensive. So, the aim of this study was continuous estimation of lower limb joint angles during walking using an accelerometer and random forest (RF). Thus, 73 subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway, which was covered with 10 Vicon camera. Acceleration was used as input for a RF to estimate ankle, knee, and hip angles (in transverse, frontal, and sagittal planes). Pearson correlation coefficient (r) and Mean Square Error (MSE) were computed between the experimental and estimated data. Paired statistical parametric mapping (SPM) t-test was used to compare the experimental and estimated data throughout gait cycle. The results of this study showed that the MSE of joint angles between the experimental and estimated data ranged from 0.04 to 24.29 and r > 0.91. Moreover, the findings of SPM indicated that there was no significant difference between the experimental and estimated data of ankle, knee, and hip angles in all three planes throughout gait cycle. The results of our research developed a more accessible, portable procedure to quantifying lower limb joint angles by an accelerometer and RF. So, such wearable-based joint angles have the potential to be used in outside-laboratory settings to measure walking kinematics.

MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition

In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models.

Continuous Estimation of Human Knee Joint Angles by Fusing Kinematic and Myoelectric Signals

Exoskeleton robot is an essential tool in active rehabilitation training for patients with lower limb motor dysfunctions. Accurate and real-time recognition in human motion intention is a great challenge in exoskeleton robot, which can be implemented by continues estimation of human joint angles. In this study, we innovatively proposed a novel feature-based convolutional neural network-bi-directional long-short term memory network (CNN-BiLSTM) model to predict the knee joint angles more accurately and in real time. We validated our method on a public dataset, including surface electromyography(sEMG) and inertial measurement unit (IMU) data of 10 healthy subjects during normal walking. Initially, features extraction from each modal data achieved feature-level fusion. Then the importance of each sEMG and IMU signal feature for knee joint angle prediction was quantified by ensemble feature scorer (EFS) and the number of features required for prediction while ensuring accuracy was simplified by profile likelihood maximization (PLM) algorithm. Finally, the CNN-BiLSTM model was created by using the determined simplest features to further fuse the spatio-temporal correlation of signals. The results indicated that the EFS and PLM algorithm could remove the feature redundancy perfectly and estimation performance would become better when bi-modal gait data were fused. For the estimation performance, the average root mean square error (RMSE), adjusted [Formula: see text] and pearson correlation coefficient (CC) of our algorithm were 4.07, 0.95, and 0.98, respectively, which was better than CNN, BiLSTM and other three traditional machine learning methods. In addition, the model test time was 62.47 ± 0.29 ms, which was less than the predicted horizon of 100 ms. The real-time performance and accuracy are satisfactory. Compared with previous works, our method has great advantages in feature selection and model design, which further improves the prediction accuracy. These promising results demonstrate that the proposed method has considerable potential to be applied to exoskeleton robot control.

Prediction of knee trajectory based on surface electromyogram with independent component analysis combined with support vector regression

In recent years, surface electromyogram signals have been increasingly used to operate wearable devices. These devices can aid to help workers or soldiers to lower the load in the task to boost efficiency. However, achieving effective signal prediction has always been a challenge. It is critical to use an appropriate signal preprocessing method and prediction algorithm when developing a controller that can accurately predict and control human movements in real time. For this purpose, this article investigates the effect of various surface electromyogram preprocessing methods and algorithms on prediction results. Walking data (surface electromyogram angle) were collected from 10 adults (5 males and 5 females). To investigate the effect of preprocessing methods on the experimental results, the raw surface electromyogram signals were grouped and subjected to different preprocessing (bandpass/principal component analysis/independent component analysis, respectively). The processed data were then imported into the random forest and support vector regression algorithm for training and prediction. Multiple scenarios were combined to compare the results. The independent component analysis-processed data had the best performance in terms of convergence time and prediction accuracy in the support vector regression algorithm. The prediction accuracy of knee motion with this scheme was 94.54% ± 2.98. Notably, the forecast time was halved in comparison to the other combinations. The independent component analysis algorithm’s “blind source separation” feature effectively separates the original surface electromyogram signal and reduces signal noise, hence increasing prediction efficiency. The main contribution of this work is that the method (independent component analysis + support vector regression) has the potency of best prediction of surface electromyogram signal for knee movement. This work is the first step toward myoelectric control of assisted exoskeleton robots through discrete decoding.

Continuous motion estimation of lower limbs based on deep belief networks and random forest

Due to the lag problem of traditional sensor acquisition data, the following movement of exoskeleton robots can affect the comfort of the wearer and even the normal movement pattern of the wearer. In order to solve the problem of lag in exoskeleton motion control, this paper designs a continuous motion estimation method for lower limbs based on the human surface electromyographic (sEMG) signal and achieves the recognition of the motion intention of the wearer through a combination of the deep belief network (DBN) and random forest (RF) algorithm. First, the motion characteristics of human lower limbs are analyzed, and the hip-knee angle and sEMG signal related to lower limb motion are collected and extracted; then, the DBN is used in the dimensionality reduction of the sEMG signal feature values; finally, the motion intention of the wearer is predicted using the RF model optimized by the genetic algorithm. The experimental results show that the root mean square error of knee and hip prediction results of the combined algorithm proposed in this article improved by 0.2573° and 0.3375°, respectively, compared to the algorithm with dimensionality reduction by principal component analysis, and the single prediction time is 0.28 ms less than that before dimensionality reduction, provided that other conditions are exactly the same.

EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TR_AG) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

A New Force Myography-Based Approach for Continuous Estimation of Knee Joint Angle in Lower Limb Amputees and Able-Bodied Subjects

In this paper, we present a new method for estimating knee joint angle using force myography. The technique utilized force myogram signals from thigh muscles while subjects walked on a treadmill at different speeds, i.e., slow, medium, fast, and run. An eight-channel in-house force myography (FMG) data acquisition system was developed to collect the data wirelessly from seven healthy subjects and a transfemoral amputee. An artificial neural network was employed to estimate the knee joint angle from force myogram signals. The root-mean-square error across the healthy subjects was 6.9±1.5° at slow (1.5 km/hr), 6.5±1.3° at medium (4 km/hr), 7.4±2.2° at fast (6 km/hr) speeds, and 8.1±2.2° while running (8 km/hr). The root-mean-square error, across the trials, for the transfemoral amputee was 4.0±1.2° at slow (1 km/hr), 3.2±0.6° at medium (2 km/hr) and 3.8±0.9° at fast (3 km/hr) speeds. The proposed approach is useful in real-time gait analysis. The system is easily wearable, convenient in out-door use, portable, and commercially viable.

Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.