Sensors

Physiological signals like heart rate and skin conductance collected from wearable devices open up a range of interesting research for clinicians and psychologists, including studying physiological reactivity to daily events and stressors. We introduce a new R package and application for analyzing wearable physiological data which enables large scale processing, and ease of use in gaining insight into this data.

During a zero-gravity parabolic flight, we recorded participants’ heart rate, accelerometer, and skin conductance measurements as well as their self-report nausea, anxiety, and excitement. Statistical analysis revealed that skin conductance is predictive of nausea, while heart rate is predictive of anxiety and excitement.

We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.

Predicting signals like stress and health depends on collecting noisy data from a number of modalities, e.g. smartphone data, or physiological data from a wrist-worn sensor. Our method can continue making accurate predictions even when a modality goes missing; for example, if the person forgets to wear their sensor.

We use labels provided by domain experts to classify whether artifacts are present in an Electrodermal Activity signal. Through the use of active learning, we improve sample efficiency and reduce the burden on human experts by as much as 84%, while offering the same or improved performance.

Ambulatory measurement of Electrodermal Activity (EDA) from the wrist has important clinical benefits, such as predicting mood, stress, health, or even seizures. However, ambulatory measurement is noisy, and artifacts can easily be mistaken for true Skin Conductance Responses (SCRs). In addition to our paper which describes a machine learning method for detecting artifacts with 95% test accuracy, we built EDA Explorer, an open-source tool that allows users to automatically detect artifacts and SCRs within their data.

We train machine learning models to predict students’ happiness from extensive data comprising physiological signals, location, smartphone logs, and behavioral questions. Analyzing which features provide the highest information gain reveals that skin conductance during sleep, social interaction, exercise, and fewer phone screen hours are all positively associated with happiness.

We trained machine learning models to classify happy vs. sad moods in college students using data from surveys and wearable sensors. Factors such as poor health-related behavior, more academic activity hours, and more neutral social interactions were highly predictive of mood.

SNAPSHOT was a large-scale study of college undergraduates which tracked detailed longitudinal data from smartphones, wearable sensors, behavioral data, and mental health and sleep quality surveys. This initial study analyzed relationships between sleep quality, stress, and GPA, and used machine learning to predict these indices from objective phone and sensor data.

We propose a method for removing motion artifacts from Electrodermal Activity using a stationary wavelet transform. We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level and skin conductance responses. Our method achieves a greater reduction of artifacts while retaining motion-artifact-free data.