The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-Living Accelerometer Data

Poor sleep and sedentary behavior patterns increase the risk of chronic diseases and negatively impact an individual's health and quality of life. Large-scale surveillance studies can unobtrusively measure free-living physical activities, sedentary behaviors, and sleep using wearable sensors; however, many human activity recognition algorithms cannot reliably detect activities in true free-living settings because they are trained on data collected in a controlled, lab setting. We describe the data collection protocol and present the first release of a multimodal, multi-sensor-site dataset (PAAWS R1). The PAAWS R1 release includes 4 hours of semi-naturalistic activities from 252 individuals and 7 days of 24-hour, free-living activities from 20 adults. We have annotated waking day activities using video to provide second-by-second, ground-truth labels capturing short, quickly changing bouts of activity with realistic activity transitions. Additionally, we have labeled up to two nights of sleep stages from PSG data collected during some nights of the free-living protocol. The PAAWS dataset enables researchers to directly compare activity recognition algorithms on the same participants' data across multiple collection protocols and days of free-living behaviors, encouraging convergence towards robust algorithms that could aid health research and drive novel mobile computing interventions and applications.