SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection
Author:
Affiliation:
1. University of California,Department of Electrical Engineering and Computer Science,Irvine,California,United States
2. University of California,Department of Mechanical and Aerospace Engineering,Irvine,California,United States
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9881599/9881528/09881725.pdf?arnumber=9881725
Reference18 articles.
1. Scalable Deep Learning for Stress and Affect Detection on Resource-Constrained Devices
2. Multi-target affect detection in the wild
3. Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?
4. HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception
5. AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices
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1. Context-Aware Approaches in IoT-based Healthcare Systems using Deep Learning Techniques: A Study;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05
2. Improved method for stress detection using bio-sensor technology and machine learning algorithms;MethodsX;2024-06
3. Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices;IEEE Internet of Things Journal;2023-08-15
4. Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity;Sensors;2023-07-25
5. A Review of Context-Aware Machine Learning for Stress Detection;IEEE Consumer Electronics Magazine;2023
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