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DC Field | Value | Language |
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dc.contributor.author | Ching, Hong Yan (程康恩) | en_US |
dc.contributor.author | Choi, Chi Kuen (蔡智權) | en_US |
dc.date.accessioned | 2014-06-26T04:51:27Z | |
dc.date.accessioned | 2017-09-19T09:19:01Z | |
dc.date.accessioned | 2019-02-12T08:40:44Z | - |
dc.date.available | 2014-06-26T04:51:27Z | |
dc.date.available | 2017-09-19T09:19:01Z | |
dc.date.available | 2019-02-12T08:40:44Z | - |
dc.date.issued | 2014-04 | en_US |
dc.identifier.other | ee2014-001 | en_US |
dc.identifier.uri | http://144.214.8.231/handle/2031/7289 | - |
dc.description.abstract | The HKICT award-winning project focuses on the hardware design of motion sensor based microcontroller devices and software for human motion detection and pattern classification. The initiative is to build a low-cost device that can capture falling down pattern of an elderly and initiate calls for help automatically, in response to the report of Center for Health Protection which enlisted falling down as the second deadliest killer in 2013. With the realization of this goal, chance of delayed rescue should be lowered. Besides this goal, the device can also be used to capture and analyze various motion patterns, therefore, health reports, tips on sleep patterns, and even calories burnt in exercise could be produced to improve the user’s fitness. For hardware, four models have been developed and each focused on a different transmission method and sensor. This is to provide a full range of options dealing with motion detection and recognition. The transmission methods implemented are Bluetooth, SD-card storage and Wi-Fi, and sensors implemented are accelerometer & Inertial Measurement Unit (IMU). For Bluetooth & Wi-Fi, they aim for real-time analysis. One is for emergency calling using mobile phone, and the other aims for data acquisition in a flexible way. With SD-card storage, it is possible to do motion recording for a long period of sampling with low power consumption. For software, terminals on PC, mobile device and a web server application have been built to receive data and perform real-time analysis. Graph plotter with filters is developed to display output and have an in depth analysis on motion pattern recorded. Machine learning algorithms are used to classify different motion patterns. | en_US |
dc.rights | This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner. | en_US |
dc.rights | Access is restricted to CityU and other institutions for the purpose of scholarly communication. | en_US |
dc.title | Mobile device and cloud server based intelligent health monitoring systems | en_US |
dc.type | Project | en_US |
dc.contributor.department | Department of Electronic Engineering | en_US |
dc.description.award | Won the Certificate of Merit for Best Student Invention (College & Undergraduates) Award at the Hong Kong ICT Awards 2014 organized by by the Office of the Government Chief Information Officer, HKSAR. | en_US |
dc.description.fulltext | Award winning work is available. | en_US |
dc.description.supervisor | Prof. Hong, Yan; Dr. Chan, Leanne L. H. | en_US |
Appears in Collections: | Student Works With External Awards |
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File | Size | Format | |
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award_winning_work.html | 163 B | HTML | View/Open |
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