Active learning for electrodermal activity classification

To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained. Active learning is a technique for reducing human effort by developing a classifier that can intelligently select the most relevant data samples and ask for labels for only those samples, in an iterative process. In this paper we demonstrate that active learning can reduce the labeling effort required of researchers by as much as 84% for our application, while offering equivalent or even slightly improved machine learning performance.MIT Media Lab ConsortiumRobert Wood Johnson Foundatio

Wavelet-based motion artifact removal for electrodermal activity

Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data

Predicting students' happiness from physiology, phone, mobility, and behavioral data

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.MIT Media Lab ConsortiumRobert Wood Johnson Foundation (Wellbeing Initiative)National Institutes of Health (U.S.) (Grant R01GM105018)Samsung (Firm)Natural Sciences and Engineering Research Council of Canad

Lets Talk about Race: Identity, Chatbots, and AI

Why is it so hard for chatbots to talk about race? This work explores how the biased contents of databases, the syntactic focus of natural language processing, and the opaque nature of deep learning algorithms cause chatbots difficulty in handling race-talk. In each of these areas, the tensions between race and chatbots create new opportunities for people and machines. By making the abstract and disparate qualities of this problem space tangible, we can develop chatbots that are more capable of handling race-talk in its many forms. Our goal is to provide the HCI community with ways to begin addressing the question, how can chatbots handle race-talk in new and improved ways

SmileTracker

This paper presents a system prototype designed to capture naturally occurring instances of positive emotion during the course of normal interaction with a computer. A facial expression recognition algorithm is applied to images captured with the user's webcam. When the user smiles, both a photo and a screenshot are recorded and saved to the user's profile for later review. Based on positive psychology research, we hypothesize that the act of reviewing content that led to smiles will improve positive affect, and consequently, overall wellbeing. We conducted a preliminary user study to test this hypothesis, as well as to gather feedback on the initial design.Robert Wood Johnson Foundation (Wellbeing Initiative

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

© 2010-2012 IEEE. While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights

Automatic identification of artifacts in electrodermal activity data

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.MIT Media Lab ConsortiumSamsung (Firm)National Institutes of Health (U.S.) (NIH grant R01GM105018)Natural Sciences and Engineering Research Council of CanadaSeventh Framework Programme (European Commission) (People Programme (Marie Curie Actions), FP7/2007-2013/ under REA grant agreement #327702

Vomit Comet Physiology: Autonomic Changes in Novice Flyers

This exploratory study examined the effects of varying g-forces, including feelings of weightlessness, on an individual's physiology during parabolic flight. Specifically, we collected heart rate, accelerometer, and skin conductance measurements from 16 flyers aboard a parabolic flight using wearable, wireless sensors. The biosignals were then correlated to participant reports of nausea, anxiety, and excitement during periods of altered g-forces. Using linear mixed-effects models, we found that (1) heart rate was positively correlated to individuals' self-reported highest/lowest periods of both anxiety and excitement, and (2) bilateral skin conductance asymmetry was positively correlated to individuals' self-reported highest/lowest periods of nausea

Prediction of Happy-Sad mood from daily behaviors and previous sleep history

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.MIT Media Lab ConsortiumSamsung (Firm)National Institutes of Health (U.S.) (NIH grant R01GM105018)National Institutes of Health (U.S.) (NIH grant T32HL007901)National Institutes of Health (U.S.) (NIH grant K99HL119618)National Institutes of Health (U.S.) (NIH grant K24HL105664

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%