2018 Progress Report: Sensible Home: Micro-environmental control through wearable personal sensorsEPA Grant Number: SU836940
Title: Sensible Home: Micro-environmental control through wearable personal sensors
Investigators: Wang, Julian , Fan, Howard , Dwivedula, Chanakya , Feng, Yanxiao , Yakkali, Sai Santosh , Duan, Qiuhua
Current Investigators: Wang, Julian , Fan, Howard
Institution: University of Cincinnati
EPA Project Officer: Callan, Richard
Project Period: February 1, 2017 through January 31, 2019 (Extended to January 31, 2022)
Project Period Covered by this Report: February 1, 2018 through January 31,2019
Project Amount: $75,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet - Phase 2 (2016) Recipients Lists
Research Category: P3 Challenge Area - Chemical Safety , Sustainable and Healthy Communities , P3 Awards
Our long-term research goal is to bring the human-in-the-loop of residential building controls using personal wearable sensors and lead to new advances in building energy efficiency and indoor comfort. As a step toward this, this P3 Phase II project targets the senior populations who were found to have significant demands regarding indoor comfort and health, develops a reliable integrative comfort model upon wearable sensor data, and determines the methods taking energy saving considerations into account. The experimental testing of the developed prototype consisting of a personal wearable module and a computing module connected to a laptop is planned for a senior living residence, in collaboration with a local non-profit continuing care retirement community. Built upon the theoretical demonstration in Phase I, the ultimate goal of this Phase II project is to improve and verify our concept and prepare it for implementation, in an effort to benefit individual indoor comfort (people), promote economic growth in smart buildings and healthcare (prosperity), and build energy efficiency (the planet).
There are two major research tasks performed in this phase. First, the team has been testing the data sensing and processing procedure using personal wearable sensors for indoor comfort purposes within the controllable mockup space at the University of Cincinnati campus. This mockup space has separated HVAC control systems, tunable ceiling lights, and an exterior window. A pilot study with 5 users has been conducted. It is found that the micro-environment of users has a more accurate prediction on users' comfort, especially in winter in which exterior windows have more impacts on perimeter zonal thermal conditions. To understand the potential impacts, the research team also studied the combined effects of air vent placements and window properties on the occupants’ thermal comfort. Second, as we demonstrated in the Phase I study, understanding occupant’s behaviors and lifestyles may facilitate the process bringing users into the building control control. Thus, the team also investigated a method predicting occupants’ behaviors and lifestyles via studying their previous utility data using data mining techniques. The techniques have been developed and tested in three senior living settings.
The current project focuses on data sensing and processing methods using the developed wearable sensors. The user testing results demonstrated the feasibility of this method and also verified the needs of additional data, including photosensors and image sensors for users’ visual information collection. Meanwhile, in the pilot study, we also found possible multi-sensory effects on the individual indoor comfort. For instance, users may prefer slightly lower temperatures in a brighter luminous environment compared with the temperature preference in dim environments. More well-controlled experiments with more samples are needed.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other project views:||All 16 publications||6 publications in selected types||All 6 journal articles|
|| Li J, Qi M, Duan Q, Huo L, Wang J. Towards Pedestrian Microclimatic Comfort: A Rapid Predication Model for Street Winds and Pedestrian Thermal Sensation. Nano Life 2018;8(02):1840006.