Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer’s, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people’s daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user’s home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to gene..., Data was collected from robot sensors during deployment of the robots in user homes over a period of 3 weeks, using a sampling technique called ecological momentary assessment (EMA) in order to generate realistic real-word interaction data., , # Real-World Human-Robot Interaction Data with Robotic Pets in User Homes in the United States and South Korea

The study included 26 participants, 13 from South Korea and 13 from the United States. The participants were drawn from the general population aged 20-35 and living alone, approximately 70% of the sample was female. The robot included sensors that could detect light, sound, movement, indoor air quality, and other environmental health data in the vicinity of the robot (please refer to associated published papers for details). While sensor data was collected via the collars, self-reported interaction behavior modalities were collected simultaneously using the Expiwell EMA mobile app ().

## Description of the data and file structure

Sensor data from the robot (\"feature\" data) was collected roughly 9 times per second, every minute of every day, across the three- week deployment period.  Meanwhile, the interaction modality data (\"target\" data) was collected via the EMA app rand...