WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
The paper presents WISE-HAR, an ensemble deep learning framework for recognizing human activities using WiFi signals. It addresses challenges such as performance variance and small dataset size through innovative techniques like ensemble learning and data augmentation. The results demonstrate high accuracy and strong generalization capabilities, making it suitable for real-world applications.
- ▪WISE-HAR recognizes three activities: 'No Presence', 'Walking', and 'Walking + Arm-waving'.
- ▪The ensemble model achieved a test accuracy of 94.87% on the Line-of-Sight scenario.
- ▪Data augmentation improved Random Forest performance from 60% to 95%.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.02974 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2606.02974 (cs) [Submitted on 2 Jun 2026] Title:WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition Authors:Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad View a PDF of the paper titled WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition, by Maheen Arshad and 2 other authors View PDF HTML (experimental) Abstract:Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
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