scholarly journals Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1318
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Lower Limb ◽  
Neural Control ◽  
Feature Selection Method ◽  
Muscle Synergy ◽  
Complex Nature ◽  
Motion Recognition ◽  
Analysis System ◽  
Knee Pathology ◽  
Semg Signals ◽  
Non Negative Matrix Factorization

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects’ health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

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.

Consistency of muscle synergies during pedaling across different mechanical constraints

2011 ◽  
Vol 106 (1) ◽  
pp. 91-103 ◽  
Author(s):  
François Hug ◽  
Nicolas A. Turpin ◽  
Antoine Couturier ◽  
Sylvain Dorel
Keyword(s):  
Neural Control ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Mechanical Constraints ◽  
Relative Contribution ◽  
Lower Limb Muscles ◽  
Emg Patterns ◽  
Mean Variance ◽  
Relative Weighting ◽  
Non Negative Matrix Factorization

The purpose of the present study was to determine whether muscle synergies are constrained by changes in the mechanics of pedaling. The decomposition algorithm used to identify muscle synergies was based on two components: “muscle synergy vectors,” which represent the relative weighting of each muscle within each synergy, and “synergy activation coefficients,” which represent the relative contribution of muscle synergy to the overall muscle activity pattern. We hypothesized that muscle synergy vectors would remain fixed but that synergy activation coefficients could vary, resulting in observed variations in individual electromyographic (EMG) patterns. Eleven cyclists were tested during a submaximal pedaling exercise and five all-out sprints. The effects of torque, maximal torque-velocity combination, and posture were studied. First, muscle synergies were extracted from each pedaling exercise independently using non-negative matrix factorization. Then, to cross-validate the results, muscle synergies were extracted from the entire data pooled across all conditions, and muscle synergy vectors extracted from the submaximal exercise were used to reconstruct EMG patterns of the five all-out sprints. Whatever the mechanical constraints, three muscle synergies accounted for the majority of variability [mean variance accounted for (VAF) = 93.3 ± 1.6%, VAF muscle > 82.5%] in the EMG signals of 11 lower limb muscles. In addition, there was a robust consistency in the muscle synergy vectors. This high similarity in the composition of the three extracted synergies was accompanied by slight adaptations in their activation coefficients in response to extreme changes in torque and posture. Thus, our results support the hypothesis that these muscle synergies reflect a neural control strategy, with only a few timing adjustments in their activation regarding the mechanical constraints.

scholarly journals Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Qiaoqin Li ◽  
Yongguo Liu ◽  
Jiajing Zhu ◽  
Zhi Chen ◽  
Lang Liu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Upper Limb ◽  
Time Domain ◽  
Recognition Performance ◽  
Feature Selection Method ◽  
Selection Method ◽  
Sensor Data ◽  
Motion Recognition ◽  
Wrapper Method ◽  
Motion Types

BACKGROUND For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm. OBJECTIVE This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance. METHODS Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method. RESULTS Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively. CONCLUSIONS The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.

scholarly journals A kinematics analysis of the lower limb during running with different sports shoes

2018 ◽  
Vol 233 (1) ◽  
pp. 46-52
Author(s):  
Lin Fu ◽  
Yaodong Gu ◽  
Qichang Mei ◽  
Julien S Baker ◽  
Justin Fernandez
Keyword(s):  
Lower Limb ◽  
External Rotation ◽  
Statistical Significance ◽  
Frontal Plane ◽  
3D Motion Analysis ◽  
Significant Difference ◽  
Running Shoes ◽  
Knee Abduction ◽  
Analysis System ◽  
Sports Shoes

The study aimed to investigate the differences in lower limb joint angles during running with three different sports shoes: basketball shoes, football shoes, and running shoes. Fifteen male subjects (age: 25 ± 2.2 years, height: 1.79 ± 0.05 m, and mass: 70.8 ± 3.4 kg) were asked to run on a treadmill at their preferred running speed. The Vicon 3D motion analysis system was used to capture the kinematics of the lower extremity during running. A one-way analysis of variance was used to determine whether any statistical significance existed between the three types of shoes (α < 0.05). Significant differences existed in the lower limb joints between the three sports shoes, particularly at the knee joint. Running shoes presented more knee flexion than basketball shoes and football shoes. In the frontal plane, basketball shoes showed less knee abduction than running shoes and football shoes. No significant difference occurred in ankle external rotation between basketball shoes and football shoes, and both of them presented greater range of motion of the ankle and knee than running shoes.

An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology

2018 ◽  
Vol 26 (3) ◽  
pp. 675-686 ◽  
Author(s):  
Ganesh R. Naik ◽  
S. Easter Selvan ◽  
Sridhar P. Arjunan ◽  
Amit Acharyya ◽  
Dinesh K. Kumar ◽  
...  
Keyword(s):  
Lower Limb ◽  
Limb Movements ◽  
Knee Pathology

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

2020 ◽  
Vol 17 (2) ◽  
pp. 026029 ◽  
Author(s):  
Dharmendra Gurve ◽  
Denis Delisle-Rodriguez ◽  
Maria Romero-Laiseca ◽  
Vivianne Cardoso ◽  
Flavia Loterio ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Matrix Factorization ◽  
Lower Limb ◽  
Channel Selection ◽  
Subject Specific ◽  
Non Negative Matrix Factorization

The Evaluation of Foot Rockers on the Kinematic Parameters of Individuals With Diabetes

2017 ◽  
Vol 11 (4) ◽  
pp. 322-329 ◽  
Author(s):  
Mohammad Taghi Karimi
Keyword(s):  
Range Of Motion ◽  
Motion Analysis ◽  
Lower Limb ◽  
P Value ◽  
Motion Analysis System ◽  
Kinematic Parameters ◽  
Hip Joints ◽  
Plantar Surface ◽  
Analysis System ◽  
Discernible Difference

Background: A variety of shoe modifications have been used to reduce the forces applied on the plantar surface of the foot in those with diabetes. Toe and heel rockers are 2 of the most common types used. The aim of this study is to evaluate the effect of these shoe modifications on the kinematics of both normal and diabetic individuals. Method: Two groups of healthy and diabetic individuals were recruited for this study. The Qualysis motion analysis system was used to record the motions of participants while walking with shoes with toe and a combination of toe and heel rockers (combined). The effects of the type of rockers used and the effect of groups were determined using MANOVA. Results: Results of the study demonstrated no discernible difference between the spatiotemporal and range of motion of the ankle, knee, and hip joints while walking with a toe and combined rockers. There was also no difference between healthy and diabetic individuals in relation to these parameters (P value >.05). Conclusion: Results of this study demonstrated no difference between the spatiotemporal and range of motion of lower-limb joints in healthy and diabetic individuals when walking with toe and combined rockers. Because the use of these rockers did not influence the kinematics of the joints while walking, it is recommended that they be used for this group of individuals if they influence the forces applied on the foot. Levels of Evidence: Level IV

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