Predicting Changes in Affective States using Neural Networks |
Stina Lyck Carstensen, Jens Madsen, Jan Larsen
|
| Abstract | Knowledge of patients affective state could prove to be crucial for health-care
professionals in both diagnosis and treatment, however, this requires patients to
report how they feel. In practice the sampling rate of affective states needs to be
kept low, in order to ensure that the patients can rest. Furthermore using traditional
methods of measuring affective states, is not always possible, e.g. patients can be
incapable of verbal communications. In this study we explore the prediction of
peoples self-reported affective state by measuring multiple physiological signals.
We use different Neural networks (NN) setups and compare with different multiple linear regression (MLR) setups for prediction of changes in affective states. The results showed that NN and MLR predicted the change in affective states with accuracies of 91:88% and 89:10%, respectively. |
| Type | Conference paper [With referee] |
| Conference | NIPS 2016 Workshop on Machine Learning for Health |
| Year | 2016 Month December |
| Publisher | Arxiv |
| Note | https://arxiv.org/abs/1612.00582 |
| Electronic version(s) | [pdf] |
| Publication link | http://www.nipsml4hc.ws/ |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |