Pervasive stress recognition for sustainable living - Andrey Bogomolov; Bruno Lepri; Michela Ferron; Fabio Pianesi; Alex Pentland (2014)
In this paper we provide the evidence that daily stress can be reliably recognized based on human behavior metrics derived from the mobile phone activity (call log, sms log, bluetooth interactions). We introduce an original approach for feature extraction, selection, recognition model training and discuss the experimental results based on Random Forest and Gradient Boosted Machine algorithms. Random Forest based model showed low variance comparing to the GBM-based one, thus winning the bias-variance tradeoff and preventing over-fitting, given the noisy source data. Potential impact of the technology is reducing stress and enhancing subjective well-being for sustainable living.