Adaptive network model for assisting people with disabilities through crowd monitoring and control

Falcon-Caro, A, Peytchev, E ORCID: https://orcid.org/0000-0001-5256-4383 and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2024. Adaptive network model for assisting people with disabilities through crowd monitoring and control. Bioengineering, 11 (3): 283.

Full text not available from this repository.

Abstract

Here we present an effective application of adaptive cooperative networks namely assisting disables navigating in the crowd in a pandemic or emergency situation. To achieve this, we model the crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchair to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, surrounding walls, and obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environment parameters and its efficacy verified.

Item Type:	Journal article
Publication Title:	Bioengineering
Creators:	Falcon-Caro, A., Peytchev, E. and Sanei, S.
Publisher:	MDPI AG
Date:	16 March 2024
Volume:	11
Number:	3
Identifiers:	Number Type 10.3390/bioengineering11030283 DOI 1897183 Other
Rights:	© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	24 May 2024 08:30
Last Modified:	24 May 2024 08:30
URI:	https://irep.ntu.ac.uk/id/eprint/51470

Actions (login required)

Edit View

Statistics

Views

Downloads