NDLI: Steering Control Assistance for a Powered Wheelchair using Deep Neural Frameworks

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Steering Control Assistance for a Powered Wheelchair using Deep Neural Frameworks

Content Provider	TKM College of Engineering
Advisor	Jaleel, Sumayya
Author	B., Bijeesh
Abstract	People with intellectual, motor, sensory damage always faces difficulty in their safe mobility. Thus, for their safe and secure navigation caregivers are needed always. To help these disabled persons for an independent mobility, a new deep learning technique for steering control assist for a powered wheelchair is proposed. In this work, the system learns to direct a powered wheelchair with user satisfaction in new environments. Long-Short Term Memory (LSTM), Bidirectional LSTM (BLSTM ) and Gated Recurrent Unit (GRU) neural networks are used for assisting the guidance of a powered wheelchair. The inputs to these neural networks is dataset of ranges produced by ultrasonic sensors considered to be devoted on the wheelchair and output is the six direction classes. The model predicts the wheelchair direction on the basis of the input data. Later the direction predicted by neural network techniques is been combined with user defining direction which produces the resultant direction so as to avoid objects in the path. The differently-abled person or patients, uses an input method to provide desired speed along with direction and the neural network provides a safe direction for the wheelchair avoiding objects in vicinity. A powered wheelchair model simulation is also done and results are verified with the expected outcomes. The comparison study on numerous parameters concludes that GRU model showed the best performance with overall accuracy of 97.38%. The accuracies obtained from LSTM and BLSTM models are 95.17% and 95.89% respectively.
Language	English
Publisher Date	2022-07-01
Access Restriction	Open
Subject Keyword	Bi-directional Long Short-term Memory Artificial Neural Network Artificial Intelligence AI Gated Recurrent Unit Hybrid Intelligent System Scanning Collison Avoidance Device
Content Type	Text
Resource Type	Thesis

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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Steering Control Assistance for a Powered Wheelchair using Deep Neural Frameworks

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