Understanding Ambulatory and Wearable Data for Health and Wellness
Year: 2014
Type: article
Abstract: In our research, we aim (1) to recognize human internal states and behaviors (stress level, mood and sleep behaviors etc), (2) to reveal which features in which data can work as predictors and (3) to use them for intervention. We collect multi-modal (physiological, behavioral, environmental, and social) ambulatory data using wearable sensors and mobile phones, combining with standardized questionnaires and data measured in the laboratory. In this paper, we introduce our approach and some of our projects. Introduction and Our Approach Recently, we have so many devices to monitor our daily lives: pedometer, activity monitor, and sleep monitor etc. Many people wear them to quantify their personal behaviors; however, how can we use our collected data
Authors: Akane Sano, Rosalind W. Picard
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2296025089