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  1. \section{Introduction}
  2. \label{sec:introduction}
  3. The turning over frequency while sleeping is an important index to quantify the
  4. health of elderly. Many wearable devices can also achieve the same purpose, but
  5. many study show that the elderly feel uncomfortable with wearing such devices
  6. all days. By the low resolution thermal camera, we
  7. can obtain the daily activities information, but not reveal too much privacy
  8. like the RGB camera.
  9. {\bf Contribution}
  10. In this work, we deployed multiple low resolution thermal cameras to monitor the
  11. turning over frequency while sleeping. We propose a data fusing and enhancement method
  12. to fuse multiple Grideye
  13. thermal sensors into a low-resolution thermal image, and use Super-resolution techniques
  14. to enhance the resolution. With our pose detection method, we have 65\% accuracy of pose
  15. detection, and 50\% recall rate and 83\% precision of turning over detection.
  16. %The remaining of this paper is organized as follows. Section~\ref{sec:bk_related}
  17. %presents background for developing the methods. Section~\ref{sec:design} presents
  18. %the system architecture, and the developed mechanisms. Section~\ref{sec:eval}
  19. %presents the evaluation results of proposed mechanism and Section~\ref{sec:conclusion} summaries our works.