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|Title:||Monitoring and alerting system for home-alone elderly||Authors:||Lwin, Daryl Aung.||Keywords:||DRNTU::Engineering||Issue Date:||2013||Abstract:||The aim of this project was to create a system that integrates the monitoring of human vital signs with an alert system that could inform the relevant people in times of emergencies. So far, the vital signs that can be monitored are Occurrence of a fall Breath rate Brain waves Monitoring falls relies on the fact that all Android phones have built-in accelerometers that can provide real time information. Using these accelerometers, we can then detect an impact that the user will have with the ground during a fall. Monitoring of the breath rates was a far more daunting task, because the data has to be monitored over periods of time. A circuit had to be built for this monitoring, and there had to be a balance between form and function. Several other features, such as the microphone to account for human speech, were added later in the project. The last portion of the project was to monitor the brainwaves of a person. While basic transfer of data has been accomplished by this project, further research is required in order to learn how to process the raw data in to meaningful results. As for the alerting system, we decided to use the cellular network simply because it was the most reliable and readily available resource to on the phone. Earlier versions of the app required the user to key in the phone number of the relevant personnel during the start up of the app. However, later editions of the app included the ability to connect to a central database on the Google server, and retrieve the numbers according to an ID number that the patient would be given beforehand. We also included certain recording features in the program which would store the data on a server, and can then be retrieved at a later point for further analysis.While the main purpose of this project was to create a system that would automatically monitor elderly patients, we also see applications in lie detection, since that involves the monitoring of body signs. Naturally, a trained operator will have to be present to analyze the data to detect lies. Currently, the size of the breadboard, the reliance on steady DC power supplies as well as the size of the Bluetooth module makes the project unfeasible as a commercial product. With specialized modules designed instead, the project could easily yield not only a viable product, but a useful one as well.||URI:||http://hdl.handle.net/10356/53382||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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Updated on Nov 25, 2020
Updated on Nov 25, 2020
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