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Open AccessArticle

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

Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan
Institute of Information Management, National Chung Cheng University, Chiayi 621, Taiwan
Author to whom correspondence should be addressed.
Symmetry 2017, 9(7), 125;
Received: 20 June 2017 / Revised: 11 July 2017 / Accepted: 11 July 2017 / Published: 21 July 2017
(This article belongs to the Special Issue Applications of Internet of Things)
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. View Full-Text
Keywords: Internet of Things; long-term care 2.0; image segmentation; k-means clustering; histogram Internet of Things; long-term care 2.0; image segmentation; k-means clustering; histogram
MDPI and ACS Style

Tseng, H.-T.; Hwang, H.-G.; Hsu, W.-Y.; Chou, P.-C.; Chang, I.-C. IoT-Based Image Recognition System for Smart Home-Delivered Meal Services. Symmetry 2017, 9, 125.

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