Abstract
Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.
Highlights
Many patients still depend on others to help them move their wheelchairs, and patients with limited mobility still face significant challenges when using wheelchairs in public and in other places [1]
This paper develops a new powerful, low-cost system based on voice recognition and convolutional neural network (CNN) approaches to drive a wheelchair for disabled users
A low-cost and robust method was used for designing a voice-controlled wheelchair and subsequently implemented using an Android smartphone app to connect microcontrollers via an offline Wi-Fi hotspot
Summary
Many patients still depend on others to help them move their wheelchairs, and patients with limited mobility still face significant challenges when using wheelchairs in public and in other places [1]. Using an electric wheelchair equipped with an automatic navigation and sensor system, such as a smart wheelchair, would be beneficial in addressing a significant challenge for several patients. The smart wheelchair is an electric wheelchair equipped with a computer and sensors designed to facilitate the efficient and effortless movement of patients [4,5,6,7]. These wheelchairs are considered safer and more comfortable than conventional wheelchairs because they introduce new control options, which include navigation systems (GPS) and other technologies, such as saving places on the user’s map [8,9]
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