Abstract: Individuals with impaired mobility may encounter physical limitations or even insurmountable obstacles in their daily lives. Manual or powered wheelchairs can facilitate their mobility and enhance their quality of life. However, it can be challenging for wheelchair users to navigate unfamiliar environments. Caregivers and/or care- giving devices are essential for the elderly and disabled. However, caregivers face a significant physical and temporal burden, and those receiving assistance may feel constrained in the caregiver's presence. To alleviate these burdens, autonomous care-giving devices that do not require human intervention are needed. Voice signals are the primary modality of human communication, used in all conversations and interactions. This paper presents the design of a voice-controlled automated wheelchair, which is a novel approach to provide mobility for physically disabled individuals. The design incorporates a voice recognition system that enables users to control the wheelchair including development for targeting users with hand movement impairments due to aging or paralysis by voice commands. It also discusses the application of Machine Learning Algorithms including Random Forest Algorithm and K-Nearest Neighbor algorithm to improve the accuracy and reliability of the voice recognition system. Assisting individuals with physical disabilities in detecting obstacles in their path and ensuring safe navigation to users. The software also includes health monitoring capabilities, which provide healthy suggestions and notify the user's physician or caretaker of their heartbeat, temperature, ECG, SpO2, and blood pressure.

Keywords: Voice Processing, Motor control, Obstacle detection, Machine Learning.

Works Cited:

N NANDITHA BHUSHANA, ABHISHARAN, ANUSHA HS, NIYANTH M" Design Of a Voice-Controlled Automated Wheelchair ", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 10, no. 11, pp. 59-69, 2023. Crossref https://doi.org/10.17148/IARJSET.2023.101109

| DOI: 10.17148/IARJSET.2023.101109