Bio-Sensing based Adaptive Thermal Comfort Controls

Transcription

1 Bio-Sensing based Adaptive Thermal Comfort Controls Vivian Loftness 1, Joon Ho Choi 1, Volker Hartkopf 1, Gerry Mattern 1, and Artur Dubrawski 2 1 Center for Building Performance and Diagnostics, School of Architecture, Carnegie Mellon University 2 Auton Lab., Robotics Institute, Carnegie Mellon University EXECUTIVE SUMMARY Today s growing emphasis on minimizing energy use in buildings can compromise indoor environmental quality (IEQ) and individual control of environmental conditions. Control innovations for buildings mechanical systems are critical in demonstrating that meeting the physiological needs of occupants can save energy and improve environmental quality. Most existing thermal comfort models for mechanical systems controls are designed based on the extensive regression formulas averaging thermal responses of the extensive experimental human subjects. Since the generalized models based on statistical regression already contain deviations with errors, the generated models may not work for a person who has different physiological conditions from the pre-assumed occupants. By necessity, existing comfort models disregard individual physiological characteristics such as race, age, gender, health, body fat percentage, and thereby have severe limitations in ensuring individual thermal satisfaction. While there have been many efforts to overcome the current technology and research limitations to improve individualized control, they are still based on pre-set programmable parameters and require physical access to a controller. The human body has a biological thermoregulation mechanism (homeostasis) which enables it to maintain a stable and constant body temperature by changing physiological signals including skin temperatures and heart rate. These signal patterns have the potential to provide information about an individual s current thermal comfort condition. The research will use human physiological responses to establish an adaptive thermal comfort controller, which can be triggered by an individual s unique bio-signals for automatic mechanical system controls. The research team has performed four-round human-subject experiments in the environmental chamber at the Center for Building Performance and Diagnostics of Carnegie Mellon University with 90 subjects. The team has investigated which bio-signal type is more significantly related with human thermal sensation and found a body segment which generates the most significant bio-signal to express a subject s thermal sensation. Based on the findings, an adaptive control logic has been developed to control HVAC systems to maintain the subject s thermal comfort without over-cooling and over-heating. The research outcome can substantially improve occupant s comfort, health, and well-being while advancing environmental sustainability with energy savings in any types of existing or new buildings. Page 1

2 INTRODUCTION The most popular thermal comfort formula, Predicted Mean Vote (PMV), requires environmental and human factor information to generate an optimal thermal condition. However, to accurately estimate the PMV, all sensors for temperature, air velocity, mean radiant temperature, and relative humidity should be placed in close proximity to the occupants while all the human factor information, including clothing insulation and the dynamically-changing activity level (i.e. metabolic rate), are simultaneously recorded. This is not easy to implement in a real building environment. After the PMV model was developed, many researchers found that there are significant differences between the actual mean vote and the predicted mean vote. It is mainly because the PMV model is developed based on the average subjects responses in the Fanger s experiment and on its assumptions of environmental and physiological conditions. Since its averaged outcomes were used to develop a regression-based PMV model, the model does not consider individual physiological characteristics including gender, age, race, and body fat percentage, which are currently proven to be significant human variables for thermal perception [1]. Doherty and Arens [2] found that the actual thermal sensation has a 1.3 scale difference compared to the PMV. Humpreys [3] also discovered that the PMV model is more accurate with sedentary activity and light clothing, and that the discrepancy would increase with higher activity levels and heavier clothing. The most advanced thermostat in the current market is a programmable thermostat. This was developed to go beyond the limitation of the conventional thermostat, which can get only the single set point. The programmable thermostat allows a user to select several set points based on their occupancy modes and activity patterns. However, when the user s daily schedule is not matched with the programmed settings, over-shooting can occur and result in generating energy waste. Since manual setting operates the thermostat for the most part, there exists no function to automatically override a new set point to reflect on dynamic human factors such as activity and clothing insulation. One of common physiological feature in humans is the thermoregulation mechanism which has three major steps to maintain a constant body core temperature depending on the thermal environment. These are shivering, sweating, and altering the skin temperature [4]. When human are exposed to extreme conditions where the body may gain or lose excessive heat, shivering and sweating are the primary modes in preserving the core body temperature [5] (Fig. 1). Temperature regulation center Cold (A.T.:65ºF or lower) (A.T.: 86ºF or lower) Warm Shivering Sweating Skin temperature Muscle Skin surface Generate internal heat Vasodilatation Vasoconstriction Surface evaporation BODY Body temperature Thermal environment Temperature receptors Fig. 1. Negative feedback mechanism for thermoregulation [4] Page 2

3 Thus, based on the limitations of current industry products and academic research on human physiology, this research proposes Bio-sensing Based Adaptive Thermal Comfort Controls, which use a human as an integrated sensor through monitoring his/her physiological reactions to the thermal environment. The physiological reaction data that is gathered is then used for the HVAC system controls to maintain a neutral thermal sensation, thus preventing thermal stress caused by over-heating and over-cooling (Fig. 2). Fig. 2. Conceptual diagram of the developed bio-sensing based adaptive HVAC system controls METHODS The main objective of this research is to select a body segment to generate the most responsive bio-signal type and body point, and to use the signal as an input variable for the heating and cooling system controls. Bio-signals and environmental data are measured through human-subject experiments, and the data is analyzed to investigate relationships between the overall thermal sensation and skin temperature its pattern in each local body. The findings from the analysis are used for a control logic design and system control implementation in the environmental chamber. The research uses ten body points for skin temperature and one point for heart rate measurements. The body segments were selected based on the most popularly-used skin points by fifteen existing thermoregulation models [6]. The four-round human-subject experiment was performed in the environmental chamber at Carnegie Mellon University with different ambient temperature settings. The first-round experiment is to investigate which type of bio-signals is more significant as an indicator for the occupant thermal sensation. The second and the third rounds are to find out the patterns and levels of skin temperature in heating and cooling processes respectively, and which body segment can generate the most crucial bio-signals to be used for the occupant thermal comfort estimation. To find a most generalized feature of bio-signals, each experiment uses 20 to 30 human subjects to get a statistical significance. Based on the findings from the human-subject experiments, a bio-signal adaptive control logic is developed for the building HVAC. The control system is validated through the fourth round human subject. Page 3

4 RESULTS / KEY FINDINGS The research was started with five objectives and seven hypotheses. These fundamental arguments have been verified and validated by significant findings through the human-subject experiments. The findings are as follows: 1) Thermal sensation could be changed even under an identical thermal environment with the same ambient temperature, relative humidity and air speed because there are many hidden variables that affect the occupant s thermal sensation, such as previous thermal condition, changing activity levels by minute changes in the physiological condition, a minor change of air speed, etc. 2) Subjects reported different thermal sensations even in an identical thermal environment. Since their physiological characteristics such as gender, body mass index, as well as cultural issues, have not been treated as a significant variable that affects thermal sensation in the existing PMV theory, the thermal environment could be sensed differently. 3) The most responsive bio-signal is skin temperature rather than the heart rate in moderate thermal conditions of building environments. The correlation analysis between bio-signals and thermal conditions explains the sensitivity of bio-signals to changing thermal conditions and the subject thermal sensation. The subject s heart rate seems to react more sensitively to his/her high level of activity rather than his/her thermal sensation by the ambient thermal conditions. 4) The research team identifies the most responsive body location for skin temperature measurement which indicates a subject s thermal comfort condition. Simplification of the control system is very important for the user s convenience. Since this study aims at practical applications in realenvironments, selecting a minimum number of most responsive local body point(s) that is also not intrusive is very important. As each local body has different physical features such as fat layer depth, the changing patterns of skin temperatures vary. The study found most responsive body point(s) from the 10 sampled body segments while the chamber generated different thermal environments. 5) Depending on physiological characteristics of subjects, their skin temperatures and heart rates vary even in an equivalent environment. Even though the existing thermoregulation computation models have been developed based on the wide range of human-subject experiments, the study found that each subject generates a different level of skin temperature on the same body point among the group of subjects, with a range of differential. 6) The bio-signal analysis outcomes of the collected data from the multi-round human-subject experiments provide optimal parameters of skin temperatures to estimate correct thermal sensation of the subject. Each subject generates different skin temperatures, but the team investigate for the patterns of the signals that are common across subjects. These physiological features enable the control system to estimate the occupant s thermal sensation. 7) The study will develop quantification methods of physiological benefits and energy savings of the bio-sensing controller. The performance of the developed controller will be compared with that of conventional PMV-control strategy to quantify the energy performance, and thermal comfort satisfaction through work productivity. The quantification process would be helpful for performance verification. Page 4

5 CONCLUSIONS The research outcomes propose a new-generation thermostatic control method for thermal environment with an affordable strategy which can be applicable to any type of new and existing buildings. Compared to the existing thermostatic controls with limitations on adjustability to the occupant s thermal preference by his/her physiological characteristics, the developed control meet those needs and can be implemented via wireless communication between users and building systems. It would contribute to their well-being, health and productivity. Since the bio-sensing control is principally dependent on human bio-signal status, it can prevent any excessive over-shooting conditions, and leave unoccupied spaces float to a boarder comfort band. These functional features contribute to environmental and physiological benefits by energy saving while maintaining thermal comfort. This project focuses on system controls in single occupancy room of offices, healthcare and residential buildings. In practice, there are many cases in which a single thermostat must be shared with officemates or roommates. This can generate thermal stress by different thermal preferences. However, research outcome provides a fundamental principle for system controls in multi-occupancy conditions by negotiating strategy with one set point to minimize thermal stress between the occupants. As multioccupancy control techniques would have the potential to be expanded to a whole building system to minimize energy use and occupant thermal stress, the bio-sensing control in multi-occupancy condition would be a meaningful topic as a future work. Page 5

6 ADDENDA Fig. 3. Exterior and interior views of the chamber Fig. 4. Floor plan of the environmental chamber Fig. 5. Data collection interface Page 6

7 Fig. 6. Thermal perception survey interface I Forehead Upper arm Lower arm Back of hand Chest Belly Thigh Anterior calf Posterior calf Instep of foot & Heart rate sensor on chest Fig. 7. Skin temperature sensor locations on a subject body Fig. 8. Sedentary posture of a subject Page 7

8 REFERENCES 1. Cena K, de Dear R. Thermal comfort and behavioral strategies in office buildings located in a hot-arid climate. Journal of Thermal Biology 2001; 26(4-5): Doherty TJ. Arens, E. Evaluation of the physiological bases of thermal comfort model. ASHRAE Transaction 1998; 94(1), Humpreys MA. Field studies and climate chamber experiments in thermal comfort research. In N.A. Oseland & M.A. Humpreys (Eds). Thermal Comfort: Past Present and Future (pp.52-72). Garston, UK: Building Research Establishment; Wang XL, Peterson, FK. Estimating thermal transient comfort. ASHRAE Transaction 1992;98: Wikipedia. Thermoregulation. Available from [accessed in 15/01/09]. 6. Choi JK. Miki K. et al., Evaluation of mean skin temperature formulas by infrared thermography. International Journal of Biometeology 1997; 41: Page 8