IoT-Based Image Recognition System for Smart Home-Delivered Meal Services

Hsiao-Ting Tseng, Hsin-Ginn Hwang, Wei-Yen Hsu, Pei-Chin Chou, I.-Chiu Chang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT) applications in the 10-year long-term care program 2.0. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensure that the right meals are delivered to the right recipient at the right time, the runners need to take a picture of the meal recipient when the meal is delivered. This study uses the IoT-based image recognition system to design an integrated service to improve the management of image recognition. The core technology of this IoT-based image recognition system is statistical histogram-based k-means clustering for image segmentation. However, this method is time-consuming. Therefore, we proposed using the statistical histogram to obtain a probability density function of pixels of a figure and segmenting these with weighting for the same intensity. This aims to increase the computational performance and achieve the same results as k-means clustering. We combined histogram and k-means clustering in order to overcome the high computational cost for k-means clustering. The results indicate that the proposed method is significantly faster than k-means clustering by more than 10 times.
Original languageEnglish
Article number125
Issue number7
StatePublished - Jul 2017


  • Internet of Things; long-term care 2.0; image segmentation; k-means clustering; histogram

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