A Tensor-Based Method for Completion of Missing Electromyography Data
This paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or the incapability of instruments to collect ve...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8777071/ |
| _version_ | 1818596675384508416 |
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| author | Muhammad Akmal Syed Zubair Mads Jochumsen Ernest Nlandu Kamavuako Imran Khan Niazi |
| author_facet | Muhammad Akmal Syed Zubair Mads Jochumsen Ernest Nlandu Kamavuako Imran Khan Niazi |
| author_sort | Muhammad Akmal |
| collection | DOAJ |
| description | This paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or the incapability of instruments to collect very low-amplitude signals. In many real-world EMG-related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employ tensor factorization methods to recover unstructured and structured missing data from the EMG signals. In this paper, we use the first-order weighted optimization (WOPT) of the parallel factor analysis (PARAFAC) decomposition model to recover missing data. We tested our proposed framework against non-negative matrix factorization (NMF) and PARAFAC on simulated as well as on off-line EMG signals having unstructured missing values (randomly missing data ranging from 60% to 95%) and structured missing values. In the case of structured missing data having different channels, the percentage of missing data of a channel goes up to 50% for different movements. It has been observed empirically that our proposed framework recovers the missing data with relatively much-improved accuracy in terms of relative mean error (up to 50% and 30% for unstructured and structured missing data, respectively) compared with the matrix factorization methods even when the portion of unstructured and structured missing data reaches up to 95% and 50%, respectively. |
| first_indexed | 2024-12-16T11:35:41Z |
| format | Article |
| id | doaj.art-045f830eb76f440daa56bad7fdd58148 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T11:35:41Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-045f830eb76f440daa56bad7fdd581482022-12-21T22:33:07ZengIEEEIEEE Access2169-35362019-01-01710471010472010.1109/ACCESS.2019.29313718777071A Tensor-Based Method for Completion of Missing Electromyography DataMuhammad Akmal0Syed Zubair1Mads Jochumsen2Ernest Nlandu Kamavuako3Imran Khan Niazi4https://orcid.org/0000-0001-8752-7224Department of Electrical Engineering, International Islamic University, Islamabad, PakistanDepartment of Electrical Engineering, International Islamic University, Islamabad, PakistanDepartment of Health Science and Technology, Center of Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, DenmarkDepartment of Informatics, Centre for Robotics Research, King’s College London, London, U.K.Department of Health Science and Technology, Center of Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, DenmarkThis paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or the incapability of instruments to collect very low-amplitude signals. In many real-world EMG-related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employ tensor factorization methods to recover unstructured and structured missing data from the EMG signals. In this paper, we use the first-order weighted optimization (WOPT) of the parallel factor analysis (PARAFAC) decomposition model to recover missing data. We tested our proposed framework against non-negative matrix factorization (NMF) and PARAFAC on simulated as well as on off-line EMG signals having unstructured missing values (randomly missing data ranging from 60% to 95%) and structured missing values. In the case of structured missing data having different channels, the percentage of missing data of a channel goes up to 50% for different movements. It has been observed empirically that our proposed framework recovers the missing data with relatively much-improved accuracy in terms of relative mean error (up to 50% and 30% for unstructured and structured missing data, respectively) compared with the matrix factorization methods even when the portion of unstructured and structured missing data reaches up to 95% and 50%, respectively.https://ieeexplore.ieee.org/document/8777071/EMG datamissing datatensor decomposition |
| spellingShingle | Muhammad Akmal Syed Zubair Mads Jochumsen Ernest Nlandu Kamavuako Imran Khan Niazi A Tensor-Based Method for Completion of Missing Electromyography Data IEEE Access EMG data missing data tensor decomposition |
| title | A Tensor-Based Method for Completion of Missing Electromyography Data |
| title_full | A Tensor-Based Method for Completion of Missing Electromyography Data |
| title_fullStr | A Tensor-Based Method for Completion of Missing Electromyography Data |
| title_full_unstemmed | A Tensor-Based Method for Completion of Missing Electromyography Data |
| title_short | A Tensor-Based Method for Completion of Missing Electromyography Data |
| title_sort | tensor based method for completion of missing electromyography data |
| topic | EMG data missing data tensor decomposition |
| url | https://ieeexplore.ieee.org/document/8777071/ |
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