Health

Please cite the following paper when using this dataset:

Nirmalya Thakur, Vanessa Su, Kesha A. Patel, Mingchen Shao, Hongseok Jeong, Victoria Knieling, and Andrew Bian, "A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other Sources about the 2024 Outbreak of Measles", 26th International Conference on Human-Computer Interaction, Washington DC, USA, 29 June - 4 July 2024 (Paper Submitted)

Mobile phones are central to modern communication, yet for individuals with tremors, the precision required for touch-based interfaces is a significant hurdle. In pursuit of social equality and to empower those with tremors to interact more effectively with mobile technology, this study introduces an optical see-through augmented reality (AR) system equipped with a stabilized filter.

A dataset comprising a total of 21 individuals has been meticulously compiled, with 9 individuals identified as exhibiting Major Depressive Disorder (MDD) based on the outcomes derived from the PHQ-9 Questionnaire. The remaining 12 individuals in the dataset are classified as non-MDD. 

The dataset encompasses diverse sensor data, including temperature measurements, SpO2 readings, pulse rates, and accelerometer data. It is important to note that all data points were collected within a controlled environment, ensuring reliability and consistency throughout the dataset.

We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.