Decoding of Hand Motion Using State of Art Time Domain, Frequency Domain And Feature Extraction / (Record no. 608613)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02526nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Munawar, Sulaiman |
| 245 ## - TITLE STATEMENT | |
| Title | Decoding of Hand Motion Using State of Art Time Domain, Frequency Domain And Feature Extraction / |
| Statement of responsibility, etc. | Sulaiman Munawar |
| 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 | 69p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Exoskeletons that are activated by the muscles and brain have been suggested to train the<br/>motor skills of stroke victims. Training can incorporate task variety since an exoskeleton allows<br/>for the execution of various movement types.Differentiating between movement types at the<br/>same time from brain activity is challenging, but it might be accessible from residual muscular<br/>activity that many patients retain regain.This study examines whether forearm EMG from five<br/>stroke patients can be used to decode seven distinct motion classes of the hand and forearm. This<br/>study evaluates classifiers like Support vector machine (SVM), Lineardiscriminant analysis<br/>(LDA) and K nearest neighbor (KNN). It investigated the relation of motor impairment with<br/>classification accuracy by the classifiers. During the following motion classes: Supination,<br/>Pronation, Hand Close, Hand Open, Wrist Extension, Wrist Flexion, and Pich, five surface EMG<br/>channels were recorded.Every motion was performed by patients three times repetition over the<br/>course of eight weeks.Support vector machines, k nearest neighbor, and linear discriminant<br/>analysis were used to classify decoding of hand moments for stroke patients. On average,73.69 ±<br/>6.39%SVM,71.6 ± 5.09% KNNand 50±4.56 LDA of the movements were correctly<br/>classified.Seven motion classes were demonstrated to be decoded from residual EMG, and SVM<br/>proved to be the most effective classification method when compared to the other three<br/>classifiers for decoding of hand motion for stroke patients.The results of this study may have<br/>implications for the development of exoskeletons, suits, or gadgets, that are powered by EMG<br/>signals. These devices might be utilized in the comfort of the patient's home to assist stroke<br/>sufferers with their training activities. Therefore, the findings of this study may assist in<br/>improving the effectiveness and accessibility of these useful tools for stroke survivors. |
| 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/123456789/42464">http://10.250.8.41:8080/xmlui/handle/123456789/42464</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 | 03/07/2024 | 610 | SMME-TH-997 | Thesis |