scholarly journals Online Adaptive Prediction of Human Motion Intention Based on sEMG

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2882
Author(s):  
Zhen Ding ◽  
Chifu Yang ◽  
Zhipeng Wang ◽  
Xunfeng Yin ◽  
Feng Jiang
Keyword(s):  
Feature Extraction ◽  
Human Motion ◽  
Muscle Synergy ◽  
Prediction Algorithm ◽  
Heel Strike ◽  
Motion Prediction ◽  
Initial Contact ◽  
Semg Signal ◽  
Online Adaptation ◽  
Motion Intention

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

scholarly journals Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 33
Author(s):  
Elisa Digo ◽  
Mattia Antonelli ◽  
Valerio Cornagliotto ◽  
Stefano Pastorelli ◽  
Laura Gastaldi
Keyword(s):  
Human Motion ◽  
Body Motion ◽  
Upper Body ◽  
Prediction Algorithm ◽  
Future Perspective ◽  
Motion Prediction ◽  
Upper Limbs ◽  
Human Motion Prediction ◽  
Pick And Place ◽  
Robotic Applications

(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.

Mirror motion recognition method about upper limb rehabilitation robot based on sEMG

2021 ◽  
pp. 1-9
Author(s):  
Lin Li
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Limb Movement ◽  
Muscle Synergy ◽  
Rehabilitation Robot ◽  
Semg Signal ◽  
Motion Recognition ◽  
Upper Limb Rehabilitation ◽  
Mirror Motion ◽  
Semg Signals

A novel method of mirror motion recognition by rehabilitation robot with multi-channels sEMG signals is proposed, aiming to help the stroked patients to complete rehabilitation training movement. Firstly the bilateral mirror training is used and the model of muscle synergy with basic sEMG signals is established. Secondly, the constrained L1/2-NMF is used to extracted the main sEMG signals information which can also reduce the limb movement characteristics. Finally the relationship between sEMG signal characteristics and upper limb movement is described by TSSVD-ELM and it is applied to improve the model stability. The validity and feasibility of the proposed strategy are verified by the experiments in this paper, and the rehabilitation robot can move with the mirror upper limb. By comparing the method proposed in this paper with PCA and full-action feature extraction, it is confirmed that convergence speed is faster; the feature extraction accuracy is higher which can be used in rehabilitation robot systems.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

RPCA-Based High Resolution Through-the-Wall Human Motion Feature Extraction and Classification

2021 ◽  
pp. 1-1
Author(s):  
Qiang An ◽  
Shuoguang Wang ◽  
Lei Yao ◽  
Wenji Zhang ◽  
Hao Lv ◽  
...  
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Human Motion ◽  
Motion Feature

scholarly journals Multitask Non-Autoregressive Model for Human Motion Prediction

2020 ◽  
pp. 1-1
Author(s):  
Bin Li ◽  
Jian Tian ◽  
Zhongfei Zhang ◽  
Hailin Feng ◽  
Xi Li
Keyword(s):  
Autoregressive Model ◽  
Human Motion ◽  
Motion Prediction ◽  
Human Motion Prediction
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