Real-Time Gait Monitoring and On-Demand Assistance for Enhanced Accessibility

DOI

10.2991/978-94-6463-866-0_15How to use a DOI?

Keywords

Gait Analysis; Abnormal Gait Detection; Model Training; Real-Time Assistance; Queue Avoidance; Security Notification

Abstract

Gait abnormalities can significantly impact an individual’s mobility and independence, necessitating timely assistance and intervention. Gait analysis plays a crucial role in healthcare and assistive technology, helping identify mobility impairments and providing necessary support. However, existing gait detection systems often lack real-time accessibility and seamless integration with public assistance services, creating challenges for individuals with abnormal gait.

To address this issue, this project proposes an application that combines automated gait detection with real-time assistance services. The system captures a user’s gait patterns, analyzes them using a trained model, and classifies the gait as normal or abnormal. Upon detecting an abnormal gait, the individual can register in the application and request specific assistance, such as wheelchair support, queue avoidance, or transportation services at public places. The security teams at these locations receive real-time notifications to provide necessary assistance. The system is designed with a structured backend for processing and a user-friendly interface to ensure seamless interaction.

This application has broad implications in healthcare, smart infrastructure, transportation hubs, and accessibility services, enhancing the quality of life for individuals with mobility impairments. Performance evaluation of the system shows an accuracy of 98% approximately in gait classification. By bridging the gap between real-time gait analysis and immediate support, this system aims to improve accessibility and foster a more inclusive environment for individuals with mobility challenges.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. V. Ajita Jane
AU  - J. Raja Sekar
AU  - G. R. Subasree
AU  - Sridevi Sridhar
PY  - 2025
DA  - 2025/10/31
TI  - Real-Time Gait Monitoring and On-Demand Assistance for Enhanced Accessibility
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 152
EP  - 164
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_15
DO  - 10.2991/978-94-6463-866-0_15
ID  - Jane2025
ER  -