Development of a Multilingual Voice-Controlled Smart Wheelchair with Advanced Features

Locomotion is a basic necessity for every individual. The weak, the elderly, and the specially abled encounter numerous challenges while commuting short distances, let alone long distances. Thus, they require mobility aids like wheelchairs. Since most of the wheelchairs are mechanical, it puts great toil on them to roll the wheels by themselves. Over the past few years, some electrical wheelchairs were either built from scratch or modified from the conventional mechanical ones to make the movement effortless which were either remote-controlled, which burdened the user with the need to learn how to use it, or voice-controlled, which lacked security features like the user-specific voice command input. Moreover, the commercially produced voice-controlled wheelchairs are for only English-speaking persons, expensive and lack functionalities and safety features like obstacle avoidance, emergency braking, and distress calls. The resolve of this research is to modify a conventional mechanical wheelchair into an electrically powered and user-specific voice-controlled wheelchair with the necessary safety features, which will be a more cost-effective and technologically sound alternative to the prevailing solutions making its users independent. Besides being user-specific, the developed voice-control system is language modular and accent-independent. Neglecting the computationally expensive deep learning models, a minimal and cost-effective voice recognition module which uses MFCC based feature extraction and GMM classifier is chosen. As the mother tongue of the developers of this product is Bengali, so it was decided to program to recognize Bengali voice commands. The proposed system can recognize Bengali voice commands with an overall average accuracy of 91.2% in a closed room over different commands in Bengali and function accordingly. The users of this wheelchair can be monolingual and do not necessarily have to learn another language to operate it.

Authors

Institutions

Publication Details

Published
2025-02-13
DOI
https://doi.org/10.1109/ecce64574.2025.11013935
Citations
1
Primary Topic
Mobile and Web Applications
Type
article
Field-Weighted Citation Impact
3.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a Multilingual Voice-Controlled Smart Wheelchair with Advanced Features

Shaikh Anowarul Fattah, Kamel M. Hassan, Sadad Hasan, Shourov Joarder et al.
1 citations
Mobile and Web Applications
3.25
article

Development of a Multilingual Voice-Controlled Smart Wheelchair with Advanced Features

Shaikh Anowarul Fattah, Kamel M. Hassan, Sadad Hasan, Shourov Joarder, Al Nayem
article en
1 citations

Abstract

Locomotion is a basic necessity for every individual. The weak, the elderly, and the specially abled encounter numerous challenges while commuting short distances, let alone long distances. Thus, they require mobility aids like wheelchairs. Since most of the wheelchairs are mechanical, it puts great toil on them to roll the wheels by themselves. Over the past few years, some electrical wheelchairs were either built from scratch or modified from the conventional mechanical ones to make the movement effortless which were either remote-controlled, which burdened the user with the need to learn how to use it, or voice-controlled, which lacked security features like the user-specific voice command input. Moreover, the commercially produced voice-controlled wheelchairs are for only English-speaking persons, expensive and lack functionalities and safety features like obstacle avoidance, emergency braking, and distress calls. The resolve of this research is to modify a conventional mechanical wheelchair into an electrically powered and user-specific voice-controlled wheelchair with the necessary safety features, which will be a more cost-effective and technologically sound alternative to the prevailing solutions making its users independent. Besides being user-specific, the developed voice-control system is language modular and accent-independent. Neglecting the computationally expensive deep learning models, a minimal and cost-effective voice recognition module which uses MFCC based feature extraction and GMM classifier is chosen. As the mother tongue of the developers of this product is Bengali, so it was decided to program to recognize Bengali voice commands. The proposed system can recognize Bengali voice commands with an overall average accuracy of 91.2% in a closed room over different commands in Bengali and function accordingly. The users of this wheelchair can be monolingual and do not necessarily have to learn another language to operate it.

Bangladesh University of Engineering and Technology (BD)
Openalex Percentile: Top 9%
Mobile and Web Applications
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Development of a Multilingual Voice-Controlled Smart Wheelchair with Advanced Features — Shaikh Anowarul Fattah, Kamel M. Hassan, et al. · (2025) | TGRS Research Map | TGRS