scholarly journals Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals

2018 ◽  
Vol 8 (9) ◽  
pp. 1462 ◽  
Author(s):  
Xianfu Zhang ◽  
Shouqian Sun ◽  
Chao Li ◽  
Zhichuan Tang
Keyword(s):  
Control System ◽  
Lower Limb ◽  
Gait Recognition ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Real Time Control ◽  
Lower Limb Exoskeleton ◽  
Load Variation ◽  
Significant Difference

As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man–machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.

scholarly journals The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8365
Author(s):  
Xianfu Zhang ◽  
Yuping Hu ◽  
Ruimin Luo ◽  
Chao Li ◽  
Zhichuan Tang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Lower Limb ◽  
Gait Recognition ◽  
Training Set ◽  
Lower Limb Exoskeleton ◽  
The Impact ◽  
Semg Signals

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.

scholarly journals Development of human lower limbs exoskeleton robot for medical rehabilitation

2021 ◽  
pp. 91-97
Author(s):  
E. A. Kotov ◽  
◽  
A. D. Druk ◽  
D. N. Klypin ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Experimental Research ◽  
Lower Limb ◽  
Lower Limbs ◽  
Medical Rehabilitation ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Controlled Motion ◽  
And Control

The article deals with the solution of the problem of optimizing the characteristics of controlled motion of human lower limb exoskeleton robot for improving medical rehabilitation. The aim of the work is to develop a rehabilitation device capable of providing controlled motion in two planes, as well as maintaining balance without loss of mobility. The design and control system of a rehabilitation trainer designed for performing mechanotherapy of the lower limbs of patients with locomotive disorders are proposed and characterized. The developed system has a number of significant differences from analogues and can be recommended for experimental research on patients with impaired locomotive functions

scholarly journals Android Malware Detection using Machine Learning

2020 ◽  
Vol 8 (2S12) ◽  
pp. 65-70
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
User Interest ◽  
Android Malware ◽  
Android Malware Detection

Machine Learning is empowering many aspects of day-to-day lives from filtering the content on social networks to suggestions of products that we may be looking for. This technology focuses on taking objects as image input to find new observations or show items based on user interest. The major discussion here is the Machine Learning techniques where we use supervised learning where the computer learns by the input data/training data and predict result based on experience. We also discuss the machine learning algorithms: Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support Vector Machine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset. Android is an operating system that is gaining popularity these days and with a rise in demand of these devices the rise in Android Malware. The traditional techniques methods which were used to detect malware was unable to detect unknown applications. We have run this dataset on different machine learning classifiers and have recorded the results. The experiment result provides a comparative analysis that is based on performance, accuracy, and cost.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 84070-84081 ◽  
Author(s):  
Susanna Yu. Gordleeva ◽  
Sergey A. Lobov ◽  
Nikita A. Grigorev ◽  
Andrey O. Savosenkov ◽  
Maxim O. Shamshin ◽  
...  
Keyword(s):  
Control System ◽  
Real Time ◽  
Lower Limb ◽  
Human Machine Interface ◽  
Lower Limb Exoskeleton ◽  
Machine Interface

scholarly journals Overview: Types of Lower Limb Exoskeletons

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1283 ◽  
Author(s):  
Daniel S Pamungkas ◽  
Wahyu Caesarendra ◽  
Hendawan Soebakti ◽  
Riska Analia ◽  
Susanto Susanto
Keyword(s):  
Control System ◽  
Lower Limb ◽  
Work Activities ◽  
Lower Limb Exoskeleton ◽  
The Future ◽  
Review Study

Researchers have given attention to lower limb exoskeletons in recent years. Lower limb exoskeletons have been designed, prototype tested through experiments, and even produced. In general, lower limb exoskeletons have two different objectives: (1) rehabilitation and (2) assisting human work activities. Referring to these objectives, researchers have iteratively improved lower limb exoskeleton designs, especially in the location of actuators. Some of these devices use actuators, particularly on hips, ankles or knees of the users. Additionally, other devices employ a combination of actuators on multiple joints. In order to provide information about which actuator location is more suitable; a review study on the design of actuator locations is presented in this paper. The location of actuators is an important factor because it is related to the analysis of the design and the control system. This factor affects the entire lower limb exoskeleton’s performance and functionality. In addition, the disadvantages of several types of lower limb exoskeletons in terms of actuator locations and the challenges of the lower limb exoskeleton in the future are also presented in this paper.

scholarly journals Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 104 ◽  
Author(s):  
Ahmed ◽  
Yigit ◽  
Isik ◽  
Alpkocak
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

scholarly journals Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 188-208
Author(s):  
Mert Sevil ◽  
Mudassir Rashid ◽  
Mohammad Reza Askari ◽  
Zacharie Maloney ◽  
Iman Hajizadeh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Signal Processing ◽  
Feature Extraction ◽  
Psychological Stress ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Linear Discriminant ◽  
Physiological Variables

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

Sign in / Sign up

Export Citation Format

Share Document