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An Intelligent Baby Care System Based on IoT and Deep Learning Techniques

Authors: Chinlun Lai, Lunjyh Jiang


Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: Baby care system, internet of things, deep learning, machine vision.

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[1] R. Palaskar, S. Pandey, A. Telang, A. Wagh, and R. M. Kagalkar, “An Automatic Monitoring and Swing the Baby Cradle for Infant Care”, International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 12, pp. 187-189, Dec. 2015.
[2] C. Yadav, A. Pandey, M. Saraogi, and S Tribhuwan, “Active RFID and ESP8266 based Child Monitoring System”, International Journal of Computer Applications, vol. 139, no. 12, pp. 22-25, April 2016.
[3] The Infant Care System ICS - A Baby Wearable
[4] S. Hussain. “Machine learning methods for visual object detection”, Ph.D thesis of Laboratoire Jean Kuntzmann, 2011.
[5] Jui-Hung Fang(2011), “An Approach to Age Estimation Based on Facial Images and AdaBoost Algorithm” master thesis, National Taipei University.
[6] Paul Viola, and Michael Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[7] Krizhevsky, A., Sutskever, I., and Hinton, G. E. “ImageNet classification with deep convolutional neural networks”, In NIPS, pp. 1106–1114, 2012.
[8] Shr-Chi Jeng(2005), “A GMM-based Method For Dynamic Background Image Model Construction with Shadow Removal”, master thesis, National Chiao Tung University.
[9] ChinLun Lai, and ChiuYuan Tai, “A Smart Spoofing Face Detector by Display Features Analysis,” Sensors, no. 1136, vol. 16, issue 7, pp.1-15, 2016(SCI).