Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms

2020 ◽  
Vol 197 ◽  
pp. 105721
Author(s):  
Yongyu Jiang ◽  
Christine Chen ◽  
Xiaodong Zhang ◽  
Chaoyang Chen ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Muscle Activation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Activation Pattern ◽  
Muscle Activation Pattern ◽  
Shoulder Muscle

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

Finger Movement Pattern Recognition from Surface EMG Signals Using Machine Learning Algorithms

ICTMI 2017 ◽  
2019 ◽  
pp. 75-89 ◽  
Author(s):  
Shravan Krishnan ◽  
Ravi Akash ◽  
Dilip Kumar ◽  
Rishab Jain ◽  
Karthik Murali Madhavan Rathai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithms ◽  
Movement Pattern ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Finger Movement

scholarly journals A Spasticity Assessment Method for Voluntary Movement using Data Fusion and Machine Learning

2020 ◽  
Author(s):  
yan chen ◽  
Song Yu ◽  
Qing Cai ◽  
Shuangyuan Huang ◽  
Ke Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Voluntary Movement ◽  
Muscle Activation ◽  
Evaluation Method ◽  
Rating Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Stroke Patients

Abstract Background: Spasticity is a common complication of stroke. Effective spasticity management can improve patients' recovery efficiency and reduce patients' pain. The present clinical spasticity rating scale exhibits subjectivity and a ceiling effect, which makes it difficult to evaluate spasm objectively and to clinically analyze the pathological mechanism of spasticity. The sensor-based quantitative evaluation method is an effective substitute for the clinical spasm rating scale, but currently, it mainly focuses on the spasm evaluation of passive motion. The study of spasmodic state under active exercise can provide a basis for treatment and rehabilitation training, but the evaluation method of spasmodic state under active exercise has not yet been established. Therefore, we combine inertial measurement unit (IMU) and surface electromyography (sEMG) to test the feasibility of assessing spasticity patterns in stroke patients during voluntary movement. Methods: Nine stroke patients with varying degrees of spasticity and four healthy subjects performed isometric elbow exercises. sEMG and kinematics signals were recorded for all participants. The Empirical Mode Decomposition (EMD) algorithm and double threshold algorithms were used to separate sEMG of involuntary muscle activation from voluntary activation. Then, feature extraction and feature fusion were performed. Four common machine learning algorithms are used to monitor and evaluate spasticity patterns. The validity of the proposed method is verified by comparing the classification accuracy of four machine learning models. Results: Cross-validation yielded high classification accuracies (F1-score>0.88) for all four machine learning classifiers in assessing spasticity patterns. The highest detection performance was obtained using the Random Forest algorithm (average accuracy = 0.979; macro-F1 = 0.976). Conclusions: We present a novel method for assessing post-stroke spasticity based on voluntary movement and machine learning. Good classification performance verifies the feasibility of evaluating spasticity patterns by our method. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to evaluate spasticity patterns using IMU and sEMG when stroke survivors perform voluntary movements.

scholarly journals A Survey of kNN Algorithm

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Jingwen Sun ◽  
Weixing Du ◽  
Niancai Shi
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Related Literature ◽  
Machine Learning Classification ◽  
Advantages And Disadvantages ◽  
Implementation Steps

The kNN algorithm is a well-known pattern recognition method, which is one of the best text classifi cation algorithms. It is one of the simplest machine learning algorithms in machine learning classification algorithm. In this paper, we summarize the kNN algorithm and related literature, introduce the idea, principle, implementation steps and implementation code of kNN algorithm in detail, and analyze the advantages and disadvantages of the algorithm and its various improvement schemes. This paper also introduces the development of kNN algorithm, the important published papers. At the end of this paper, the application of kNN algorithm is introduced, and its implementation in text classifi cation is emphasized.

Preventing undesirable behavior of intelligent machines

Science ◽  
2019 ◽  
Vol 366 (6468) ◽  
pp. 999-1004 ◽  
Author(s):  
Philip S. Thomas ◽  
Bruno Castro da Silva ◽  
Andrew G. Barto ◽  
Stephen Giguere ◽  
Yuriy Brun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Complex Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intelligent Machines ◽  
Dangerous Behavior ◽  
Flexible Framework ◽  
Undesirable Behavior

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause harm to humans—is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

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