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Capacitive Sensors for user Gesture Recognition in Smart Environments

Rezaei Shahmirzadi, Aein | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51369 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Karbalaee Aghajan, Hamid
  7. Abstract:
  8. To create applications for smart environments we can select from a huge variety of sensors that measure environmental parameters within the premises. Capacitive proximity sensors use weak electric fields to recognize conductive objects, such as the human body. They can be unobtrusively applied or even provide information when hidden from view which make these sensor more popular. Furthermore, these sensors are low cost, precise and low power. In this thesis, we study the construction and operation of capacitive sensors and the challenges of using them. Then we use them to produce smart devices. Smart flooring is used on the ground and it can be used to track people or fall detection. Smart key can be used hidden of view for turning on or off light without touching it and generally use it as a switch. The next smart device is the smart surface that use twelve capacitive sensors to detect twelve different hand movements with 95% accuracy which can be used inside wall or table. In the next step we use ten sensors to make smart chair. We detect 21 different sitting posture with 99.8% accuracy. The data analysis tool in this study is machine learning. Finally, by using smart chair we propose a new mothod for data collection to obtain breath rate in both deep breathing and regular breating. The proposed method and results in smart chair and smart surface have outperformed the method and results suggested in the previous studies, and the achieved results are comparable to those of the state-of-the-art studies
  9. Keywords:
  10. Machine Learning ; Capacitance Sensor ; Smart Home ; Gesture Recognition

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