ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1612928
This article is part of the Research TopicAdvancements in Smart Diagnostics for Understanding Neurological Behaviors and Biosensing ApplicationsĀ - Volume IIView all articles
Generalising Location-Centric Variations to Enhance Contactless Human Activity Recognition
Provisionally accepted- 1Coventry University, Coventry, United Kingdom
- 2Glasgow Caledonian University, Glasgow, Scotland, United Kingdom
- 3Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
- 4Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- 5Heriot-Watt University, Edinburgh, Scotland, United Kingdom
- 6Al Ain University, Al Ain, Abu Dhabi, United Arab Emirates
- 7University of Glasgow, Glasgow, Scotland, United Kingdom
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Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behaviour, detect falls or abnormal activities in real time.The effectiveness of non-invasive HAR is often hindered by location-centric variations in Channel State Information (CSI). These variations limit the ability of HAR models to generalise across new unseen cross-domain environments, for instance a model trained in one location might not perform well in another physical location. To address this challenge, in this paper, we present a novel Federated Learning (FL) algorithm designed to train a robust global model from local datasets in different localisations. The proposed Federated Weighted Averaging for HAR (Fed-WAHAR) algorithm mitigates location-induced disparities, including heterogeneity and non-Independent and Identically Distributed (non-IID) data distributions. Fed-WAHAR employs a dynamic weighting approach based on local models' accuracy to improve global model classification accuracy and reduce convergence time effectively. We evaluated the performance of Fed-WAHAR using various metrics, including accuracy, precision, recall, F1 score, confusion matrix, and convergence analysis. Experimental results demonstrate that Fed-WAHAR achieves an accuracy of 85% in recognising human activities across different locations, enhancing the ability of model to infer across new unseen locations.
Keywords: Federated learning, Human activity recognition, non-independent and identically distributed (non-iid) data, localisation, Weighted averaging
Received: 16 Apr 2025; Accepted: 05 Jun 2025.
Copyright: Ā© 2025 KHAN, Yaseen Shah, Ahmad, Al Mazroa, Zahid, Ilyas, Abbasi and Shah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: FAWAD KHAN, Coventry University, Coventry, United Kingdom
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