EMG Feature Reduction Technique For Optimal Accuracies / (Record no. 607334)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02505nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Abbas, Usman |
| 245 ## - TITLE STATEMENT | |
| Title | EMG Feature Reduction Technique For Optimal Accuracies / |
| Statement of responsibility, etc. | Usman Abbas |
| 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 | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 94p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The recording of electrical activity which is produced by muscles is known as an<br/>Electromyogram or Electromyographic (EMG) signal. The generation of electric current during<br/>the contraction of muscles is measured by it. The insight of muscles dynamics and neural<br/>activation is provided by EMG signal and is thus significant for several different applications,<br/>such as the studies that try to identify deficiencies of neuromuscular. For researchers and<br/>practitioners, signal of EMG is very important to observe and evaluate the muscles condition and<br/>the outcome of the rehabilitation training. The signal of EMG features precision and factors vary<br/>correspondingly with signal of muscle, fatigue, and features.<br/>The hand movements classification based on signals of surface electromyography<br/>(sEMG) is a key problem in assistive devices and rehabilitation system control. The<br/>classification of movements of hand from sEMG is a method that has different applications like<br/>rehabilitation, interaction of human-machine and prosthetic control. The main issue is that by<br/>using increase number of features and channels of EMG in order to maximize the number of<br/>control commands can produce a feature vector of high dimensional. The major challenge is the<br/>process development to predict the current motion robustly and accurately based on incoming<br/>sEMG data. To overcome the problems of accuracy and computation linked with high dimension<br/>vector, feature reduction technique is applied that converts the data to low dimension vector<br/>space with a bit loss of valuable informative data.<br/>The aim of this thesis is to extract features and to reduce its dimensionality using PCA to<br/>improve classification success rate and compare the findings of classification accuracy before<br/>and after applied PCA technique. Six different classifiers were used on the EMG data before and<br/>after using feature reduction technique and a comparative study of finding is presented in this<br/>thesis study. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 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/123456789/34505">http://10.250.8.41:8080/xmlui/handle/123456789/34505</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 | 12/13/2023 | 610 | SMME-TH-869 | Thesis |