A Comparative Analysis of Different Features for EMG Signal Classification / (Record no. 610734)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02552nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ikram, Zainab |
| 245 ## - TITLE STATEMENT | |
| Title | A Comparative Analysis of Different Features for EMG Signal Classification / |
| Statement of responsibility, etc. | Zainab Ikram |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 72p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Electromyography (EMG) signals serve as vital tools in neurological and<br/>neuromuscular conditions diagnosis. Various features are used as inputs for pattern<br/>recognition algorithms. This project intends to increase the precision and efficacy of<br/>prosthetic limb control, with the goal of boosting the quality of life for individuals with<br/>limb amputations, using a Linear Support Vector Machine technique. Specifically, we<br/>intend to analyze the usefulness of the distinctive feature known as Cardinality within<br/>diverse combinations of time-domain and frequency-domain features. In order to improve<br/>signal quality, the raw EMG signal is filtered and segmented. The time-domain and<br/>frequency-domain features are then retrieved from overlapping segments, and the most<br/>relevant ones are retained using exhaustive feature selection. An SVM classifier is then<br/>used to examine the possible impact of Cardinality on prosthetic control and<br/>rehabilitation outcomes. The research findings show that the efficiency of Cardinality is<br/>dependent on the precision of the units used. Cardinality performed best when seven<br/>decimal points are used. MAV stands out among time-domain features, as it generated a<br/>high number of combinations with Cardinality, enhancing its performance in myoelectric<br/>pattern recognition and BP emerges as the top-performing frequency-domain feature<br/>when integrated with Cardinality, surpassing other frequency-domain features and<br/>forming the most numerous combinations. The SVM classifier achieved classification<br/>accuracy of 85.58% of M1, 70.49% of M2, 77.32% of M3, 77.24% of M4, 80.82% of<br/>M5, 77.52% of M6, 82.94% of M7, 84.34% of M8, 84.75% of M9, 86.92% of M10 for<br/>the combination of Cardinality with MAV and BP. As advancements in prosthetics and<br/>rehabilitation technologies continue, the insights gained from this study can play a pivotal<br/>role in refining the precision and efficiency of Myoelectric Control systems, ultimately<br/>benefiting individuals with limb loss or motor impairments |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Asim Waris |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/45023">http://10.250.8.41:8080/xmlui/handle/123456789/45023</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 08/02/2024 | 610 | SMME-TH-1034 | Thesis |