scholarly journals Real-time classification of muscle signals from self-selected intentional movements

2021 ◽  
Author(s):  
Kaveh Seyed Momen
Keyword(s):  
Real Time ◽  
Feature Space ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Natural Logarithm ◽  
Emg Signal ◽  
Feature Spaces ◽  
Cluster Validity Indices ◽  
Validity Indices

A novel method to automatically differentiate forearm movements has been proposed. The electromyography (EMG) signals were recorded from two muscle sites on the forearm in real-time. Two 2-dimensional feature spaces namely the natural logarithm of root-mean-square values (Log (RMS)), and the standard deviations of auto regressive model coefficients (Stdev (AR)) were created. The features were calculated within non-overlapping 0.2 second windows in real-time. The feature spaces were clustered using the fuzzy c-means algorithm [1]. The cluster multiplicities were investigated by five different cluster validity indices. Real-time EMG signal classification was achieved by calculating membership values. Log (RMS) performed superior to the Stdev (AR) feature space. The silhouette validity index provided the best cluster validity index in this study. On average, the proposed algorithm classified 4 movements with 92.7± 3.2% and 5 movements with 79.90%±16.8% accuracy. The algorithm also revealed the number of repeatable movements. It can also be adapted to daily variations in individual EMG signals.

scholarly journals Real-time classification of muscle signals from self-selected intentional movements

2021 ◽  
Author(s):  
Kaveh Seyed Momen
Keyword(s):  
Real Time ◽  
Feature Space ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Natural Logarithm ◽  
Emg Signal ◽  
Feature Spaces ◽  
Cluster Validity Indices ◽  
Validity Indices

A novel method to automatically differentiate forearm movements has been proposed. The electromyography (EMG) signals were recorded from two muscle sites on the forearm in real-time. Two 2-dimensional feature spaces namely the natural logarithm of root-mean-square values (Log (RMS)), and the standard deviations of auto regressive model coefficients (Stdev (AR)) were created. The features were calculated within non-overlapping 0.2 second windows in real-time. The feature spaces were clustered using the fuzzy c-means algorithm [1]. The cluster multiplicities were investigated by five different cluster validity indices. Real-time EMG signal classification was achieved by calculating membership values. Log (RMS) performed superior to the Stdev (AR) feature space. The silhouette validity index provided the best cluster validity index in this study. On average, the proposed algorithm classified 4 movements with 92.7± 3.2% and 5 movements with 79.90%±16.8% accuracy. The algorithm also revealed the number of repeatable movements. It can also be adapted to daily variations in individual EMG signals.

A new cluster validity index using maximum cluster spread based compactness measure

2016 ◽  
Vol 9 (2) ◽  
pp. 179-204 ◽  
Author(s):  
M. Arif Wani ◽  
Romana Riyaz
Keyword(s):  
Optimal Number ◽  
Data Sets ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Data Set ◽  
Number Of Clusters ◽  
Content Type ◽  
Validity Indices ◽  
Optimal Number Of Clusters

Purpose – The most commonly used approaches for cluster validation are based on indices but the majority of the existing cluster validity indices do not work well on data sets of different complexities. The purpose of this paper is to propose a new cluster validity index (ARSD index) that works well on all types of data sets. Design/methodology/approach – The authors introduce a new compactness measure that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster. A novel penalty function is proposed for determining the distinctness measure of clusters. Random linear search-algorithm is employed to evaluate and compare the performance of the five commonly known validity indices and the proposed validity index. The values of the six indices are computed for all nc ranging from (nc min, nc max) to obtain the optimal number of clusters present in a data set. The data sets used in the experiments include shaped, Gaussian-like and real data sets. Findings – Through extensive experimental study, it is observed that the proposed validity index is found to be more consistent and reliable in indicating the correct number of clusters compared to other validity indices. This is experimentally demonstrated on 11 data sets where the proposed index has achieved better results. Originality/value – The originality of the research paper includes proposing a novel cluster validity index which is used to determine the optimal number of clusters present in data sets of different complexities.

MiniMax ε-stable cluster validity index for Type-2 fuzziness

2012 ◽  
Vol 184 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Ibrahim Ozkan ◽  
I. Burhan Türkşen
Keyword(s):  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Stable Cluster

scholarly journals Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

2014 ◽  
Vol 37 (1) ◽  
pp. 141-157 ◽  
Author(s):  
Mariusz Łapczyński ◽  
Bartłomiej Jefmański
Keyword(s):  
Predictive Models ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Model Combining ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
And Cluster Analysis ◽  
Analytical Tools ◽  
F Measure

Abstract Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

2020 ◽  
Vol 25 (6) ◽  
pp. 755-769
Author(s):  
Noorullah R. Mohammed ◽  
Moulana Mohammed
Keyword(s):  
Data Clustering ◽  
Topic Models ◽  
Cluster Validity ◽  
Text Documents ◽  
Text Data ◽  
Validity Assessment ◽  
Text Document ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Data Clusters

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.

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