A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper propose...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1504 |
| _version_ | 1797395746645868544 |
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| author | Mohammed Asfour Carlo Menon Xianta Jiang |
| author_facet | Mohammed Asfour Carlo Menon Xianta Jiang |
| author_sort | Mohammed Asfour |
| collection | DOAJ |
| description | ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement. |
| first_indexed | 2024-03-09T00:38:59Z |
| format | Article |
| id | doaj.art-f16b69ab90444142899b2cd4525c19e5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:38:59Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f16b69ab90444142899b2cd4525c19e52023-12-11T17:57:29ZengMDPI AGSensors1424-82202021-02-01214150410.3390/s21041504A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic VarianceMohammed Asfour0Carlo Menon1Xianta Jiang2Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaBiomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zürich, 8008 Zürich, SwitzerlandDepartment of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.https://www.mdpi.com/1424-8220/21/4/1504force myographyhand gestures recognitionmachine learningdata pre-processing |
| spellingShingle | Mohammed Asfour Carlo Menon Xianta Jiang A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance Sensors force myography hand gestures recognition machine learning data pre-processing |
| title | A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance |
| title_full | A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance |
| title_fullStr | A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance |
| title_full_unstemmed | A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance |
| title_short | A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance |
| title_sort | machine learning processing pipeline for reliable hand gesture classification of fmg signals with stochastic variance |
| topic | force myography hand gestures recognition machine learning data pre-processing |
| url | https://www.mdpi.com/1424-8220/21/4/1504 |
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