Multi-Gesture Decoding using Hybrid EMG-IMU for Rehabilitation Applications / (Record no. 610646)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03612nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shahzad, Waseem |
| 245 ## - TITLE STATEMENT | |
| Title | Multi-Gesture Decoding using Hybrid EMG-IMU for Rehabilitation Applications / |
| Statement of responsibility, etc. | Waseem Shahzad , |
| 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 | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 161p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Prosthetic rehabilitation of the upper limb through powered prosthetic devices is an active research<br/>area for the past several decades. Multi-degree of freedom prosthetic hands controlled by surface<br/>electromyography signals (sEMG) of the forearm muscles have been developed to restore the lost<br/>limb functionality. A major objective of the current research is the development of intuitive<br/>prosthetic controllers for modern dexterous prosthetic hands. Current state of the art prosthetic<br/>controllers utilize pattern recognition (PR) of multi-channel sEMG signals for decoding the<br/>intended motion class. Despite decades of academic research, a clinically viable PR based<br/>prosthetic controller is yet to be realized.<br/>The PR based classifiers have resulted in more than 90% classification accuracy when evaluated<br/>under controlled laboratory conditions. However, the excellent laboratory performance of these<br/>classifiers is yet to result in their clinical acceptability and commercial availability. As a result, the<br/>currently available prosthetic devices are still based on non-intuitive binary or sequential digital<br/>control. There are several reasons for this academia-industry disparity. The unintended variations<br/>of sEMG signal characteristic due to several confounding factors, including arm position,<br/>adversely affects the performance of pre-trained PR based prosthetic controllers. Researchers have<br/>proposed fusion of auxiliary sensory information for classifier robustness against forearm<br/>positional variations. Sensor fusion techniques including accelerometer-mechanomyography<br/>(ACC-MMG), force-myography (FMG) and magnetic markers (MMs), have been reported with<br/>significant performance improvements.<br/>This study focused on the impact of arm position variations on the performance of PR-based<br/>forearm motion class decoders. A wearable data acquisition system was designed to acquire multichannel sEMG and measure arm position using inertial measurement units. The performance of<br/>support vector machine (SVM) and linear discriminant analysis (LDA) classifiers was evaluated<br/>for training at static positions and for dynamic arm movements to characterize the adverse effects<br/>of arm position variation. Sensor fusion of sEMG and arm position data was evaluated to mitigate<br/>the arm position effect. A comparison of static multi-position training and dynamic arm movement<br/>training was carried out to suggest more pragmatic strategies for classifier training. The sensitivity<br/>xi<br/>of classifiers to motion class taxonomy was also evaluated. Sixteen motion classes categorized in<br/>distant-taxonomy and close-taxonomy groups were classified using LDA, Linear SVM (LSVM),<br/>Non-Linear SVM (NLSVM) and Multi-layer perception (MLP) classifiers. The results of the study<br/>have shown a significant dependence of LDA and LSVM classifiers and an insignificant<br/>dependence of the NLSVM and MLP classifiers on motion class taxonomy. The results suggest a<br/>more pragmatic selection of motion classes for realistic classifier performance evaluation |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PhD Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Mushtaq Khan |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/28207">http://10.250.8.41:8080/xmlui/handle/123456789/28207</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 | 07/30/2024 | 629.8 | SMME-Phd-11 | Thesis |