Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.

Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.

Air pollutants have become the major problem of many cities, causing millions of human deaths worldwide every year. Among all the noxious pollutants in air, particles with a diameter of 2.5 micrometers or less (PM2.5) are the most hazardous because they are small enough to penetrate to the lungs and invade the smallest airways. Since the presence of dangerous levels of PM2.5, commonly reported in newspapers and on TV, is intertwined with the global pattern of production and consumption, there is a need for citizen science projects that engage the young generations in efforts toward reducing air pollution as they will become the future leaders of society. With this goal, and to enable the geo-temporal characterization of PM2.5, we present a crowdsourcing-based air pollution measurement system that uses affordable DIY atomic force microscopes to measure and characterize PM2.5, exploiting the power of human computation through an online crowdsourcing platform to study how PM2.5 varies over time and across geographical locations. Our system is intended as both a scientific platform and a teaching tool for children to engage in environmental policy.