TY - GEN
T1 - Smart radiator system for enhanced thermal management using IoT and hand gesture recognition
AU - Le, Hieu Minh
AU - Ogenyi, Uchenna Emeoha
AU - Guo, Liucheng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/11/12
Y1 - 2024/11/12
N2 - This paper presents the design and implementation of an intelligent radiator system that leverages Internet of Things (IoT) technology and hand gesture recognition (HGR) to enhance thermal management and energy efficiency in residential environments. The system addresses the limitations of traditional heating systems by enabling users to control radiator temperature via a web interface and intuitive hand gestures. The system utilises a Raspberry Pi, an ESP32, and sensors (temperature, ultrasonic, and gyroscope sensors) to monitor the environment and enable precise control. The HGR functionality, powered by MediaPipe, allows for convenient, touchless adjustment of heating settings with an accuracy of 73.02%. The system’s performance is evaluated through a LEGO prototype, and potential refinements, such as dataset expansion and model optimisation, are discussed. The project demonstrates the potential of IoT technology to deliver innovative and practical solutions for energy management in smart homes. Future work envisions the integration of the system with a mobile robotic platform, enabling dynamic thermal data collection, enhanced human-robot interaction, and expanded home automation capabilities.
AB - This paper presents the design and implementation of an intelligent radiator system that leverages Internet of Things (IoT) technology and hand gesture recognition (HGR) to enhance thermal management and energy efficiency in residential environments. The system addresses the limitations of traditional heating systems by enabling users to control radiator temperature via a web interface and intuitive hand gestures. The system utilises a Raspberry Pi, an ESP32, and sensors (temperature, ultrasonic, and gyroscope sensors) to monitor the environment and enable precise control. The HGR functionality, powered by MediaPipe, allows for convenient, touchless adjustment of heating settings with an accuracy of 73.02%. The system’s performance is evaluated through a LEGO prototype, and potential refinements, such as dataset expansion and model optimisation, are discussed. The project demonstrates the potential of IoT technology to deliver innovative and practical solutions for energy management in smart homes. Future work envisions the integration of the system with a mobile robotic platform, enabling dynamic thermal data collection, enhanced human-robot interaction, and expanded home automation capabilities.
KW - Hand Gesture Recognition
KW - Internet of Things (IoT)
KW - Machine Learning
KW - MediaPipe
KW - Thermal Management
UR - https://www.scopus.com/pages/publications/85214352424
U2 - 10.1109/M2VIP62491.2024.10746202
DO - 10.1109/M2VIP62491.2024.10746202
M3 - Conference contribution
AN - SCOPUS:85214352424
SN - 9798350391923
T3 - IEEE M2VIP Proceedings
BT - 2024 30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024
Y2 - 3 October 2024 through 5 October 2024
ER -