| Author | Sabitha, Bitlla |
| Note | Thesis (M. Eng.) -- Asian Institute of Technology, 2018 |
| Publisher | Asian Institute of Technology |
| Abstract | Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In handsfree control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Therefore, the main aim of this thesis is to help the bed-ridden patients to communicate and also control the electrical appliances with the help of head movements. The patient can totally depend on themselves and with the help of their head movements, they can control basic electronic devices and also ask for their basic needs. Recently, neural networks have been shown to be useful not only for realtime pattern recognition but also for creating user-adaptive models. In this study multi-layer perceptron (MLP) neural networks trained using standard back-propagation technique has been proposed to improve the generalisation of the networks. The samples were collected and are trained and classified by the multi-layer perceptron model. The overall system accuracy is improved by optimizing the various parameters of the multilayer perceptron network. |
| Year | 2018 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Microelectronics (ME) |
| Chairperson(s) | Mongkol Ekpanyapong ; |
| Examination Committee(s) | Bohez, Erik L. J.;Abeykoon, A.M. Harsha S. ; |
| Scholarship Donor(s) | AIT Fellowship; |