Resources

TAME Pain is a dataset that captures acoustic signals of pain and is augmented by annotating every sentence the participant speaks with details such as background and foreground noise, speech errors, and non-speech vocal features.

TAME Pain is a dataset that captures acoustic signals of pain and is augmented by annotating every sentence the participant speaks with details such as background and foreground noise, speech errors, and non-speech vocal features.

A dataset of features from voice recordings and metadata to enable the development, benchmarking, and validation of clinically applicable machine-learning models for diagnosing a wide range of health conditions.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

Dataset to support comparison of usability and accuracy from simultaneous measurements collected from 13 subjects including five devices: NeXus-10 MKII, eMotion Faros 360°, Hexoskin Hx1, SOMNOTouch NIBP, Polar RS800 Multi.

An open-access database recorded during the EPHNOGRAM project, consisting of simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recordings from young healthy adults, during stress-test experiments.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.