Learning occupants' workplace interactions from wearable and stationary ambient sensing systems

Having access to real-time information on building occupants' state of interactions enables optimization of building systems for improved energy efficiency, well-being and productivity of the occupants. In this paper, we propose a framework to learn occupant interactions from ambient sensing technologies (e.g., sensing of variables such as sound (dB), CO2 (ppm), light intensity (lux), dry-bulb temperature (°C), relative humidity (RH%), pressure (mbar)) from both stationary and wearable devices and select the technologies and averaging windows which contain the required information for learning. In this framework, several supervised machine learning algorithms are tested on the labeled datasets and the algorithm which outperforms others is selected. Two types of sensing devices were utilized for analyses: wearable devices worn around the neck by the test subjects, and a network of stationary devices located in the test subjects' working indoor spaces. 221 employees of federal agencies housed in facilities managed by the US. General Services Administration in the mid-Atlantic and Southern states participated in this study, answering questions about their current task every hour. Overall accuracies were observed of 86.72% for wearable and stationary devices, 81.25% for only wearable-only, and 85.16% for stationary-only for prediction of the mixed multi-label classification via Random Forests algorithm. The high prediction allows for identifying subjects' tasks when training labels are not available. Predicting occupants' interactions as a main indicator of occupants' behavior have significant implications for the energy efficiency of building systems (up to 20% savings).