2017
DOI: 10.3390/sym9070125
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IoT-Based Image Recognition System for Smart Home-Delivered Meal Services

Abstract: 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 … Show more

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Cited by 9 publications
(4 citation statements)
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References 27 publications
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“…Different applications involving UAVs and IoT devices are proposed to perform different missions in a city environment with the aim to enhance the quality of citizens' life. For instance, these applications can manage different fields, such as transportation systems [226], agriculture [227], emergencies [228], health-care [229], smart home [230], and others (c.f., Fig. 17).…”
Section: B Smart Citiesmentioning
confidence: 99%
“…Different applications involving UAVs and IoT devices are proposed to perform different missions in a city environment with the aim to enhance the quality of citizens' life. For instance, these applications can manage different fields, such as transportation systems [226], agriculture [227], emergencies [228], health-care [229], smart home [230], and others (c.f., Fig. 17).…”
Section: B Smart Citiesmentioning
confidence: 99%
“…There are several aspects where image recognition can be applied in the smart home. In [39] was discussed meal delivery model based on image processing. However, the main trends in this field would be rather a security aspects and healthcare monitoring [40].…”
Section: Image Processing and Voice Agentsmentioning
confidence: 99%
“…Two papers on smart home are included as follows: (1) "A novel approach based on time clusters for activity recognition of daily living in smart homes", by Liu et al [48]; and (2) "IoT-based image recognition systems for smart home-delivered meal services", by Tseng et al [49].…”
Section: Smart Homementioning
confidence: 99%
“…In the proposed method, a pre-process stage was considered to analyze the discrete probability density function of the gray scale values, and to generate a discrete statistical histogram for the extraction of image features. In experimental environments, the open dataset from the "Berkeley segmentation dataset: images" provided by the University of California, Berkeley was collected to evaluate the proposed method, and the results showed that the computation time of the proposed method is lower than the k-means clustering method [49].…”
Section: Smart Homementioning
confidence: 99%