Design and Implementation of a Machine Learning Assisted Smart Wheelchair in an IoT Environment

Abstract

Gesture controlled wheelchair is designed and intended to perform specific job functions combining software interfacing with the hardware along with the embedded electronics, through hand controlled gestures. In this paper, a novel multiple control option-based wheelchair is proposed to cater to the needs of handicapped patients and senior citizens. This work is aimed at designing, integrating, coding, interfacing and testing a fully motorized, gesture controlled wheelchair which involves the physical implementation of the hardware, the software interfacing and testing. This Smart wheelchair is furnished with sensors, interactive cameras controlled by a processor unit that can control the various tasks it is designed to perform. The proposed Smart Wheelchair utilizes micro-electro-mechanical-system (MEMS) accelerometers to recognize various hand gestures performing a specific task. It is an accelerometer-based system which controls a robotic arm wirelessly using a small, low-cost, 3-axis degrees of freedom accelerometer through RF signals. The gestures are also trained by machine learning to identify the training patterns by choosing the appropriate mode selection. The hand gestures are thus based on motion sensor and pattern recognition. Apart from these hand gestures, the smart wheelchair can also be controlled by an android mobile app through a smartphone or by a hand held device that can navigate the smart wheelchair in the forward, backward, right, left directions and also perform a stop operation. The Smart Wheelchair also detects an obstruction present on the way of navigation and is operated in accordance with the command. An interactive camera equipped with a microphone and speaker is meant for communication for the elderly for sending and receiving voice commands remotely. The camera can be accessed through an IoT environment which can be communicated on remote IP addresses on smartphones and personal computers. There are two Arduino Uno Rev3 Controller modules programmed to control the gestures of the gyroscopic accelerometer and other control methods based on a mode selection switch held by the user. The proposed methodology is also realized by Proteus, a simulation tool to check its practical credibility. The proposed circuitry is physically realized as a Smart Wheelchair prototype implemented with the above multi control which could be modified as any physical structure platform having two wheels.