The ubiquitous availability of wearable sensing devices has rendered large scale collection of movement data a straightforward endeavor. Yet, annotation of these data remains a challenge and as such, publicly available datasets for human activity recognition (HAR) are typically limited in size as well as in variability, which constrains HAR model training and effectiveness. We introduce ..
Paper Abstract We present a method to analyze images taken from a passive egocentric wearable camera along with contextual information, such as time and day of the week, to learn and predict the everyday activities of an individual. We collected a dataset of 40,103 egocentric images over 6 months with 19 activity classes and demonstrate […]