Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices
Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.LG · 机器学习 · first seen 2026-11-03
Abstract
This paper presents a privacy-preserving mood monitoring framework that classifies emotional states from ECG-derived heart rate variability (HRV) features. The framework is evaluated on three public datasets comprising 74 participants and further validated using a wearable device in a pilot study. Leave-One-Participant-Out cross-validation (LOPO-CV) is used to assess generalization to unseen indiv
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Cite this page: Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices: the #42 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/privacy-preserving-ecg-hrv-emotion-classification-for-mood-swing-detection-using.html
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