Wireless, AI-enabled wearable thermal comfort sensor for energy-efficient, human-in-the-loop control of indoor temperature.

The conventional heating, ventilation, and air conditioning (HVAC) systems are based on a set-point control approach that only considers the temperature of the environment without reflecting the thermophysiological status of the occupant. This approach not only fails to fully satisfy individual thermal preferences, but it also makes an HVAC operation energy-inefficient. One possible solution is to control the indoor thermal condition based on an accurate prediction of the occupant's thermal comfort to prevent any unnecessary energy consumption. Here, we present an artificial intelligence (AI) wearable sensor-based human-in-the-loop HVAC control system that is operated on a real-time basis reflecting the thermophysiological condition of the occupant to automatically improve their thermal comfort while reducing the energy consumption of the building. The wristband-type, AI-based, three-point wearable temperature sensor offers excellent thermal comfort prediction accuracy (93.9%), enabling a human-centric HVAC control operation. A proof-of-concept demonstration of closed human-in-the-loop HVAC control using the AI-enabled wearable sensor system confirms both the accuracy of the thermal comfort prediction and the energy-efficiency of this approach, demonstrating its potential as a new solution that improves the occupant's thermal comfort and provides building energy savings.

[1]  R. Matsuhashi,et al.  Towards wearable thermal comfort assessment framework by analysis of heart rate variability , 2022, Building and Environment.

[2]  Justin Younghyun Kim,et al.  Stretchable PPG sensor with light polarization for physical activity–permissible monitoring , 2022, Science advances.

[3]  Yongming Fu,et al.  Wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators. , 2022, Biosensors & bioelectronics.

[4]  Tobias Kramer,et al.  A Machine Learning approach to enhance indoor thermal comfort in a changing climate , 2021, Journal of Physics: Conference Series.

[5]  Nikil Dutt,et al.  A novel wireless ECG system for prolonged monitoring of multiple zebrafish for heart disease and drug screening studies. , 2021, Biosensors & bioelectronics.

[6]  I. Pigliautile,et al.  Measuring human physiological indices for thermal comfort assessment through wearable devices: A review , 2021 .

[7]  Hansaem Park,et al.  Prediction of individual thermal comfort based on ensemble transfer learning method using wearable and environmental sensors , 2021, Building and Environment.

[8]  Jun Chen,et al.  Smart textiles for personalized thermoregulation. , 2021, Chemical Society reviews.

[9]  Jae‐Woong Jeong,et al.  Strain‐Isolating Materials and Interfacial Physics for Soft Wearable Bioelectronics and Wireless, Motion Artifact‐Controlled Health Monitoring , 2021, Advanced Functional Materials.

[10]  G. M. Revel,et al.  Sensing Physiological and Environmental Quantities to Measure Human Thermal Comfort Through Machine Learning Techniques , 2021, IEEE Sensors Journal.

[11]  L. Norford,et al.  Project Coolbit: can your watch predict heat stress and thermal comfort sensation? , 2020 .

[12]  Burcin Becerik-Gerber,et al.  Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods , 2020 .

[13]  Yonggang Wen,et al.  DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning , 2020, IEEE Internet of Things Journal.

[14]  Wim Zeiler,et al.  Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants' heating behavior. , 2020, Applied ergonomics.

[15]  Zhipeng Deng,et al.  Development and validation of a smart HVAC control system for multi-occupant offices by using occupants’ physiological signals from wristband , 2020 .

[16]  Yi Cui,et al.  Advanced Textiles for Personal Thermal Management and Energy , 2020, Joule.

[17]  Xiaobing Luo,et al.  Emerging Materials and Strategies for Personal Thermal Management , 2020, Advanced Energy Materials.

[18]  Hua Li,et al.  Machine learning driven personal comfort prediction by wearable sensing of pulse rate and skin temperature , 2020 .

[19]  S. Leigh,et al.  The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature , 2019, Sustainability.

[20]  Ming Jin,et al.  Personal thermal comfort models with wearable sensors , 2019, Building and Environment.

[21]  Farrokh Jazizadeh,et al.  Heat Flux Sensing for Machine-Learning-Based Personal Thermal Comfort Modeling , 2019, Sensors.

[22]  B. Becerik-Gerber,et al.  A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor , 2019, Building and Environment.

[23]  Bas J. van Ruijven,et al.  Amplification of future energy demand growth due to climate change , 2019, Nature Communications.

[24]  Kuang-Chin Lu,et al.  Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm , 2019, Building and Environment.

[25]  Rahul Simha,et al.  Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions , 2019, Building and Environment.

[26]  Yuta Suzuki,et al.  Heart rate variability as a predictive biomarker of thermal comfort , 2018, J. Ambient Intell. Humaniz. Comput..

[27]  Lihua Xie,et al.  Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology , 2018 .

[28]  Young-Ho Cho,et al.  Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring , 2018, Scientific Reports.

[29]  Lihua Xie,et al.  Thermal comfort prediction using normalized skin temperature in a uniform built environment , 2018 .

[30]  F. Shaffer,et al.  An Overview of Heart Rate Variability Metrics and Norms , 2017, Front. Public Health.

[31]  Guang-Zhong Yang,et al.  A wearable multisensing patch for continuous sweat monitoring. , 2017, Biosensors & bioelectronics.

[32]  James J S Norton,et al.  Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces , 2016, Science Advances.

[33]  Ross Baldick,et al.  Demand response control of residential HVAC loads based on dynamic electricity prices and economic analysis , 2016 .

[34]  Kwang Suk Park,et al.  Estimation of Thermal Sensation Based on Wrist Skin Temperatures , 2016, Sensors.

[35]  Hangsik Shin,et al.  Ambient temperature effect on pulse rate variability as an alternative to heart rate variability in young adult , 2015, Journal of Clinical Monitoring and Computing.

[36]  Wouter D. van Marken Lichtenbelt,et al.  Energy consumption in buildings and female thermal demand , 2015 .

[37]  Alex K. Jones,et al.  Productivity metrics in dynamic LCA for whole buildings: Using a post-occupancy evaluation of energy and indoor environmental quality tradeoffs , 2014 .

[38]  Jung Woo Lee,et al.  Multifunctional Skin‐Like Electronics for Quantitative, Clinical Monitoring of Cutaneous Wound Healing , 2014, Advanced healthcare materials.

[39]  Xian Huang,et al.  Capacitive Epidermal Electronics for Electrically Safe, Long‐Term Electrophysiological Measurements , 2014, Advanced healthcare materials.

[40]  A. A. Romanovsky,et al.  Skin temperature: its role in thermoregulation , 2014, Acta physiologica.

[41]  James J. S. Norton,et al.  Materials and Optimized Designs for Human‐Machine Interfaces Via Epidermal Electronics , 2013, Advanced materials.

[42]  Vivian Loftness,et al.  Investigation of human body skin temperatures as a bio-signal to indicate overall thermal sensations , 2012 .

[43]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[44]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[45]  Nisha Charkoudian,et al.  Skin blood flow in adult human thermoregulation: how it works, when it does not, and why. , 2003, Mayo Clinic proceedings.

[46]  W. R. Hibbard,et al.  Tensile deformation of high-purity copper as a function of temperature, strain rate, and grain size , 1953 .

[47]  Jiankang Ren,et al.  Experimental study of an indoor temperature fuzzy control method for thermal comfort and energy saving using wristband device , 2021 .

[48]  Thomas Lengauer,et al.  Permutation importance: a corrected feature importance measure , 2010, Bioinform..

[49]  Takashi Akimoto,et al.  Thermal comfort and productivity - Evaluation of workplace environment in a task conditioned office , 2010 .

[50]  L. Breiman Random Forests , 2001, Machine Learning.