Classification of Activities of Daily Living (ADLs) Based Upper Limb Movements Using Machine Learning & Neural Network Classifiers / (Record no. 607923)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02377nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Anwer, Saba |
| 245 ## - TITLE STATEMENT | |
| Title | Classification of Activities of Daily Living (ADLs) Based Upper Limb Movements Using Machine Learning & Neural Network Classifiers / |
| Statement of responsibility, etc. | Saba Anwer |
| 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 | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 68p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Real time natural control of assistive, rehabilitation and prosthetic devices has gained significant<br/>importance over the last few decades for the people suffering from motor disabilities due to<br/>stroke, any spinal cord injury or amputation. Although surface electromyography (s-EMG) has<br/>been used as a viable controlling interface for several robotic devices specifically designed for<br/>post stroke therapeutic services. But these conventional controlling strategies are not feasible to<br/>design the rehabilitation or HMI systems based on simultaneous movements of multiple degrees<br/>of freedom (DOF). This paper presents a novel control strategy for HMIs which is based on the<br/>coupled use of EMG and inertial sensors. EMG and kinematic data of healthy and stroke subjects<br/>for commonly used daily life activities has been recorded. Multiple machine learning models<br/>including LDA, QDA, LSVM, QSVM, Fine KNN, Ensembled discriminant, and ensembled<br/>KNN have been applied. Besides this a tri-layered neural network classifier has also been<br/>implemented. A comparative analysis has been performed for the classification outcomes of all<br/>the applied models for EMG, IMU & EMG+IMU data. Overall, the KNN model performed well<br/>for all types of datasets with an average accuracy of 98.5% but results clearly demonstrated that<br/>average classification accuracies for all the applied models have significantly improved for<br/>EMG+IMU data which indicates that sensor fusion based control strategy for prosthesis can<br/>achieve higher performance than conventional control systems for each task. This study is an<br/>effort to provide a new EMG+IMU based technique for fast, efficient, and reliable control of<br/>robotic, rehabilitation and assistive devices for multiple movements with varying DOF. |
| 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/123456789/31847">http://10.250.8.41:8080/xmlui/handle/123456789/31847</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 | 02/20/2024 | 610 | SMME-TH-806 | Thesis |