Gait Prediction and Mechanism Design for 1-DOF Lower Limb Rehabilitation Devices Based on Machine Learning

2021 ◽  
Author(s):  
Wanbing Song ◽  
Yating Zhang ◽  
Zhaojie Ge ◽  
Ping Zhao
Keyword(s):  
Machine Learning ◽  
Mechanism Design ◽  
Lower Limb ◽  
Support Vector ◽  
Motion Trajectory ◽  
Trajectory Prediction ◽  
Machine Method ◽  
Specific Patient ◽  
Limb Rehabilitation ◽  
Body Parameters

Abstract A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower limb rehab patients is proposed to provide the basis for the design of simple 1-degree-of-freedom (DOF) rehab mechanisms. First, a large amount of gait trajectories from various healthy volunteers are collected along with their body parameters, and a normalization method is presented to trim/expand these trajectory samples to a standard length and timing while retaining their shape and velocity information. Then, these normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithm-optimized support vector machine method is adopted to train a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, it can predict a most suitable gait trajectory for the specific patient. It shows that the accuracy of trajectory prediction reaches 96% both on training set and test set which verifies the effectiveness of the method. In the end, a 1-DOF gait rehab mechanism design example is provided to illustrate the application of the proposed method. Taking the predicted result from the classifier as the task motion trajectory for the synthesis of mechanisms, a 1-DOF six-bar mechanism is designed and the patient-mechanism matching can be realized.

scholarly journals A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

2020 ◽  
Vol 10 (8) ◽  
pp. 2638 ◽  
Author(s):  
Shuo Gao ◽  
Yixuan Wang ◽  
Chaoming Fang ◽  
Lijun Xu
Keyword(s):  
Machine Learning ◽  
Ground Reaction Force ◽  
Lower Limb ◽  
Confusion Matrix ◽  
Reaction Force ◽  
Simple System ◽  
Support Vector ◽  
Terrain Classification ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

scholarly journals Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning

2015 ◽  
Vol 9s1 ◽  
pp. CMC.S18746 ◽  
Author(s):  
Amparo Alonso-Betanzos ◽  
Verónica Bolón-Canedo ◽  
Guy R. Heyndrickx ◽  
Peter L.M. Kerkhof
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ventricular Volume ◽  
Classical Type ◽  
Curvilinear Relationship ◽  
Support Vector ◽  
Gray Zone ◽  
Training Set ◽  
Machine Method

Background Heart failure (HF) manifests as at least two subtypes. The current paradigm distinguishes the two by using both the metric ejection fraction (EF) and a constraint for end-diastolic volume. About half of all HF patients exhibit preserved EF. In contrast, the classical type of HF shows a reduced EF. Common practice sets the cut-off point often at or near EF = 50%, thus defining a linear divider. However, a rationale for this safe choice is lacking, while the assumption regarding applicability of strict linearity has not been justified. Additionally, some studies opt for eliminating patients from consideration for HF if 40 < EF < 50% (gray zone). Thus, there is a need for documented classification guidelines, solving gray zone ambiguity and formulating crisp delineation of transitions between phenotypes. Methods Machine learning (ML) models are applied to classify HF subtypes within the ventricular volume domain, rather than by the single use of EF. Various ML models, both unsupervised and supervised, are employed to establish a foundation for classification. Data regarding 48 HF patients are employed as training set for subsequent classification of Monte Carlo–generated surrogate HF patients ( n = 403). Next, we map consequences when EF cut-off differs from 50% (as proposed for women) and analyze HF candidates not covered by current rules. Results The training set yields best results for the Support Vector Machine method (test error 4.06%), covers the gray zone, and other clinically relevant HF candidates. End-systolic volume (ESV) emerges as a logical discriminator rather than EF as in the prevailing paradigm. Conclusions Selected ML models offer promise for classifying HF patients (including the gray zone), when driven by ventricular volume data. ML analysis indicates that ESV has a role in the development of guidelines to parse HF subtypes. The documented curvilinear relationship between EF and ESV suggests that the assumption concerning a linear EF divider may not be of general utility over the complete clinically relevant range.

scholarly journals Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine

2021 ◽  
pp. 070674372110371
Author(s):  
James R.A. Benoit ◽  
Serdar M. Dursun ◽  
Russell Greiner ◽  
Bo Cao ◽  
Matthew R.G. Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Rating Scale ◽  
Machine Learning Algorithms ◽  
Phase Iii ◽  
Support Vector ◽  
Individual Treatment ◽  
Major Depressive ◽  
Specific Patient

Background Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment. Methods We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data ( n = 377). Results Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%. Conclusions Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

scholarly journals Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

2022 ◽  
Author(s):  
Chiako Mokri ◽  
Mahdi Bamdad ◽  
Vahid Abolghasemi
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Muscle Force ◽  
Machine Learning Techniques ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Support Vector ◽  
Rehabilitation Robot ◽  
Learning Techniques ◽  
Semg Signals

AbstractThe main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.

Motion Synthesis for Upper-Limb Rehabilitation Motion With Clustering-Based Machine Learning Method

2019 ◽  
Author(s):  
Wenxiu Chen ◽  
Wanbing Song ◽  
Haodong Chen ◽  
Qi Li ◽  
Ping Zhao
Keyword(s):  
Machine Learning ◽  
Upper Limb ◽  
Spectral Clustering ◽  
Optimal Solution ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Motion Patterns ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Body Parameters

Abstract Nowadays, mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patient usually has its individual pattern. Thus it is obviously not an optimal solution to use a single motion generator to suit all patients. Yet it would also be unpractical if we design a different motion or even a different mechanism for each user individually. Therefore, in this paper we seek to adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Firstly, the trajectory of a specified rehabilitation motion are recorded from various subjects, and then 4 types of machine learning algorithms (spectral clustering, hierarchical clustering, self-organizing mapping neural network and Gaussian mixture model) are implemented and compared. It is shown that spectral clustering (SC) yields the best performance and is hereby adopted to generate three clusters of motion patterns. After regression of each cluster, three types of motion for upper limb-rehabilitation are constructed, which could reflect the trajectories’ similarity and difference of people who have various body parameters. These work will provide help for the design of rehabilitation mechanisms.

A Support-Vector-Machine Method for Precisely Evaluating Planar Straightness Error

2012 ◽  
Vol 220-223 ◽  
pp. 1611-1614
Author(s):  
Jiang Meng
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Evaluation Method ◽  
Support Vector ◽  
Learning Approach ◽  
Support Vector Regression Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Learning Approach ◽  
Straightness Error

To present a precise and efficient algorithm to solve planar straightness error problems, a machine-learning approach to evaluate planar straightness error was presented in this paper. According to the similarity between the least envelope zone and the support vector regression model, the SVR-based method was developed to solve the problem of straightness error. The evaluation method was compared to some existing techniques. According to the results from three datasets, it is shown that the SVR-based method can provide precise and exact values of planar straightness error.

scholarly journals The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework

2021 ◽  
Vol 13 (7) ◽  
pp. 1401
Author(s):  
Genki Okada ◽  
Luis Moya ◽  
Erick Mas ◽  
Shunichi Koshimura
Keyword(s):  
Machine Learning ◽  
News Media ◽  
Disaster Response ◽  
Large Scale ◽  
Potential Role ◽  
Training Data ◽  
Support Vector ◽  
Machine Method ◽  
Sar Imagery

When flooding occurs, Synthetic Aperture Radar (SAR) imagery is often used to identify flood extent and the affected buildings for two reasons: (i) for early disaster response, such as rescue operations, and (ii) for flood risk analysis. Furthermore, the application of machine learning has been valuable for the identification of damaged buildings. However, the performance of machine learning depends on the number and quality of training data, which is scarce in the aftermath of a large scale disaster. To address this issue, we propose the use of fragmentary but reliable news media photographs at the time of a disaster and use them to detect the whole extent of the flooded buildings. As an experimental test, the flood occurred in the town of Mabi, Japan, in 2018 is used. Five hand-engineered features were extracted from SAR images acquired before and after the disaster. The training data were collected based on news photos. The date release of the photographs were considered to assess the potential role of news information as a source of training data. Then, a discriminant function was calibrated using the training data and the support vector machine method. We found that news information taken within 24 h of a disaster can classify flooded and nonflooded buildings with about 80% accuracy. The results were also compared with a standard unsupervised learning method and confirmed that training data generated from news media photographs improves the accuracy obtained from unsupervised classification methods. We also provide a discussion on the potential role of news media as a source of reliable information to be used as training data and other activities associated to early disaster response.

scholarly journals Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Li ◽  
Xiao-Zhou Lv ◽  
Xin Zheng ◽  
Si-Min Ruan ◽  
Hang-Tong Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Differential Diagnosis ◽  
Focal Nodular Hyperplasia ◽  
Diagnostic Performance ◽  
Arterial Phase ◽  
Support Vector ◽  
Focal Liver Lesions ◽  
Machine Method ◽  
Nodular Hyperplasia

BackgroundThe typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).Patients and MethodsA total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).ResultsA total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P &lt; 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P &lt; 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P &lt; 0.001).ConclusionsMachine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.

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