Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops pp. 202 - 208
• Main Authors: Jaques, Natasha, Taylor, Sara, Sano, Akane, Picard, Rosalind
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2017
• Subjects: Data models; Mood; Noise measurement; Noise reduction; Principal component analysis; Stress; Training

To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use. Such systems are likely to encounter frequent data loss, e.g. when a phone loses location access, or when a sensor is re...