A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

Asfour, Mohammed; Menon, Carlo; Jiang, Xianta

doi:10.3390/s21041504

Cited by 15 publications

(17 citation statements)

References 43 publications

(53 reference statements)

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“…Note that inter‐class and intra‐class comparisons are known techniques (i.e., maximum inter‐class deviation and minimum intra‐class variation) in clustering and feature selection (Asfour et al, 2021; Sahu et al, 2017). However, the goal of these assessments does not help feature selection directly.…”

Section: Gigo Versus Optimal Classifiers In Machine Learningmentioning

confidence: 99%

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

Canbek

2022

WIREs Data Min & Knowl

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This article emphasizes comprehending the “Garbage In, Garbage Out” (GIGO) rationale and ensuring the dataset quality in Machine Learning (ML) applications to achieve high and generalizable performance. An initial step should be added in an ML workflow where researchers evaluate the insights gained by quantitative analysis of the datasets sample and feature spaces. This study contributes towards achieving such a goal by suggesting a technique to quantify datasets in terms of feature frequency distribution characteristics. Hence a unique insight is provided into how the features in the available dataset samples are frequent. The technique was demonstrated in 11 benign and malign (malware) Android application datasets belonging to six academic Android mobile malware classification studies. The permissions requested by applications such as CALL_PHONE compose a relatively high‐dimensional binary feature space. The results showed that the distributions fit well into two of the four long right‐tail statistical distributions: log‐normal, exponential, power law, and Poisson. Precisely, log‐normal was the most exhibited statistical distribution except the two malign datasets that were in exponential. This study also explores statistical distribution fit/unfit feature analysis that enhances the insights in feature space. Finally, the study compiles phenomena examples in the literature exhibiting these statistical distributions that should be considered for interpreting the fitted distributions. In conclusion, conducting well‐formed statistical methods provides a clear understanding of the datasets and intra‐class and inter‐class differences before proceeding with selecting features and building a classifier model. Feature distribution characteristics should be one to analyze beforehand. This article is categorized under: Technologies > Data Preprocessing Technologies > Classification Technologies > Machine Learning

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Section: Gigo Versus Optimal Classifiers In Machine Learningmentioning

confidence: 99%

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

Canbek

2022

WIREs Data Min & Knowl

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“…The offset parameters included the mean [27], standard deviation [28], variance [29], skewness [30], kurtosis [31], and central moment [32]. The mean parameter measures the central tendency of the signal probability distribution.…”

Section: Classification Featuresmentioning

confidence: 99%

IQ-Data-Based WiFi Signal Classification Algorithm Using the Choi-Williams and Margenau-Hill-Spectrogram Features: A Case in Human Activity Recognition

Lin

Yang

2021

Electronics

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This paper presents a novel approach that applies WiFi-based IQ data and time–frequency images to classify human activities automatically and accurately. The proposed strategy first uses the Choi–Williams distribution transform and the Margenau–Hill spectrogram transform to obtain the time–frequency images, followed by the offset and principal component analysis (PCA) feature extraction. The offset features were extracted from the IQ data and several spectra with maximum energy values in the time domain, and the PCA features were extracted via the whole images and several image slices on them with rich unit information. Finally, a traditional supervised learning classifier was used to label various activities. With twelve-thousand experimental samples from four categories of WiFi signals, the experimental data validated our proposed method. The results showed that our method was more robust to varying image slices or PCA numbers over the measured dataset. Our method with the random forest (RF) classifier surpassed the method with alternative classifiers on classification performance and finally obtained a 91.78% average sensitivity, 91.74% average precision, 91.73% average F1-score, 97.26% average specificity, and 95.89% average accuracy.

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“…Interpretation on muscle signals is essential for the control of electric powered prosthetic hands, which requires machine learning algorithms to classify muscular electric signals into corresponding hand movement patterns. In most of the published papers, scientists use myoelectric signals recorded during firmly grasped periods for grasp classification, which yielded satisfactory classification outcomes [ 6 , 7 , 8 , 9 , 10 , 11 ]. For instance, the research done by Jiang et al [ 7 ] using 3 s firm grasp sEMG signals achieved approximately 85% accuracy for classifying 16 grasp gestures.…”

Section: Introductionmentioning

confidence: 99%

Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG

Wang

Zheng

et al. 2022

Biosensors

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Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a sweet period before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.

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A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

Cited by 15 publications

References 43 publications

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting

IQ-Data-Based WiFi Signal Classification Algorithm Using the Choi-Williams and Margenau-Hill-Spectrogram Features: A Case in Human Activity Recognition

Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG

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