Automated Channel Selection in High-Density sEMG for Improved Force Estimation
Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4858 |
| _version_ | 1827707422393761792 |
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| author | Gelareh Hajian Ali Etemad Evelyn Morin |
| author_facet | Gelareh Hajian Ali Etemad Evelyn Morin |
| author_sort | Gelareh Hajian |
| collection | DOAJ |
| description | Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of <inline-formula><math display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> for force estimation while reducing the dimensionality by <inline-formula><math display="inline"><semantics><mrow><mn>57</mn><mo>%</mo></mrow></semantics></math></inline-formula> for a subset of three channels. |
| first_indexed | 2024-03-10T16:46:25Z |
| format | Article |
| id | doaj.art-fcbda5d6fef64e328ae09aa7620fc34b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:46:25Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fcbda5d6fef64e328ae09aa7620fc34b2023-11-20T11:39:37ZengMDPI AGSensors1424-82202020-08-012017485810.3390/s20174858Automated Channel Selection in High-Density sEMG for Improved Force EstimationGelareh Hajian0Ali Etemad1Evelyn Morin2Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, CanadaDepartment of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, CanadaDepartment of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, CanadaAccurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of <inline-formula><math display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> for force estimation while reducing the dimensionality by <inline-formula><math display="inline"><semantics><mrow><mn>57</mn><mo>%</mo></mrow></semantics></math></inline-formula> for a subset of three channels.https://www.mdpi.com/1424-8220/20/17/4858high-density electromyographyforce estimationchannel selectionfast orthogonal search |
| spellingShingle | Gelareh Hajian Ali Etemad Evelyn Morin Automated Channel Selection in High-Density sEMG for Improved Force Estimation Sensors high-density electromyography force estimation channel selection fast orthogonal search |
| title | Automated Channel Selection in High-Density sEMG for Improved Force Estimation |
| title_full | Automated Channel Selection in High-Density sEMG for Improved Force Estimation |
| title_fullStr | Automated Channel Selection in High-Density sEMG for Improved Force Estimation |
| title_full_unstemmed | Automated Channel Selection in High-Density sEMG for Improved Force Estimation |
| title_short | Automated Channel Selection in High-Density sEMG for Improved Force Estimation |
| title_sort | automated channel selection in high density semg for improved force estimation |
| topic | high-density electromyography force estimation channel selection fast orthogonal search |
| url | https://www.mdpi.com/1424-8220/20/17/4858 |
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