A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat
| Title: | A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat |
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| Authors: | Harper, Ross, Southern, Joshua |
| Publication Year: | 2019 |
| Collection: | Computer Science Statistics |
| Subject Terms: | Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Statistics - Machine Learning |
| More Details: | Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple data modalities - audio, visual, and physiological - to classify emotional state. However, in practice, collection of physiological data `in the wild' is currently limited to heartbeat time series of the kind generated by affordable wearable heart monitors. Furthermore, real-world applications of emotion prediction often require some measure of uncertainty over model output, in order to inform downstream decision-making. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat time series. We further propose a Bayesian framework for modelling uncertainty over these valence predictions, and describe a probabilistic procedure for choosing to accept or reject model output according to the intended application. We benchmarked our framework against two established datasets and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in real-world domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty. Comment: 8 pages, 2 tables |
| Document Type: | Working Paper |
| DOI: | 10.1109/TAFFC.2020.2981610 |
| Access URL: | http://arxiv.org/abs/1902.03043 |
| Accession Number: | edsarx.1902.03043 |
| Database: | arXiv |
| DOI: | 10.1109/TAFFC.2020.2981610 |
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