Electrodermal Activity

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.

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 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.