@inproceedings{
 title = {A Novel Time-Domain based Feature for EMG-PR Prosthetic and Rehabilitation Application},
 type = {inproceedings},
 year = {2019},
 keywords = {Amputees,Classification,EMG,Feature extraction,Prosthetics},
 id = {b36aa07a-92b8-32f4-9874-7cbb34529d16},
 created = {2020-01-22T23:59:00.000Z},
 file_attached = {false},
 profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},
 last_modified = {2021-03-04T04:20:01.168Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019 IEEE. EMG signal is widely accepted in human-machine interaction applications, such as prosthesis control and rehabilitation devices. The existing feature extraction methods struggle to separate a variety of EMG based activities. In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. All the analysis has been done using MATLAB 2015a in the i7 core.},
 bibtype = {inproceedings},
 author = {Pancholi, S. and Jain, P. and Varghese, A. and Joshi, A.M.},
 doi = {10.1109/EMBC.2019.8857399},
 booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}
}

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In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. All the analysis has been done using MATLAB 2015a in the i7 core.","bibtype":"inproceedings","author":"Pancholi, S. and Jain, P. and Varghese, A. and Joshi, A.M.","doi":"10.1109/EMBC.2019.8857399","booktitle":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","bibtex":"@inproceedings{\n title = {A Novel Time-Domain based Feature for EMG-PR Prosthetic and Rehabilitation Application},\n type = {inproceedings},\n year = {2019},\n keywords = {Amputees,Classification,EMG,Feature extraction,Prosthetics},\n id = {b36aa07a-92b8-32f4-9874-7cbb34529d16},\n created = {2020-01-22T23:59:00.000Z},\n file_attached = {false},\n profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},\n last_modified = {2021-03-04T04:20:01.168Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {© 2019 IEEE. EMG signal is widely accepted in human-machine interaction applications, such as prosthesis control and rehabilitation devices. The existing feature extraction methods struggle to separate a variety of EMG based activities. In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. 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