Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques
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Published:2023-02-25
Issue:5
Volume:12
Page:1132
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Arshad Jehangir1ORCID, Ashraf Muhammad Adil1, Asim Hafiza Mahnoor1, Rasool Nouman23ORCID, Jaffery Mujtaba Hussain1ORCID, Bhatti Shahid Iqbal14
Affiliation:
1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan 2. Electromagnetic Technology and Engineering Key Laboratory, Nanchong 637000, China 3. School of Electronic Information Engineering, China West Normal University, Nanchong 637000, China 4. Majan International Petroleum Services LLC, Muscat P.O. Box 598, Oman
Abstract
Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.
Funder
Basic Doctoral Research Funding of China West Normal University
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference31 articles.
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