Development Of Android Based Health Monitoring System
The large number of population within the age between 25 to 29 implied the importance of health care services to maintain their wellbeing. However, a lot of health measuring devices are only able to measure one health parameter. Besides, most of the measuring devices stored only a limited number o...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53382/ http://eprints.usm.my/53382/1/Development%20Of%20Android%20Based%20Health%20Monitoring%20System_Ooi%20Yoong%20Khang_E3_2018.pdf |
| _version_ | 1848882513692327936 |
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| author | Ooi, Yoong Khang |
| author_facet | Ooi, Yoong Khang |
| author_sort | Ooi, Yoong Khang |
| building | USM Institutional Repository |
| collection | Online Access |
| description | The large number of population within the age between 25 to 29 implied the importance of health care services to maintain their wellbeing. However, a lot of health measuring devices are only able to measure one health parameter. Besides, most of the
measuring devices stored only a limited number of records. Furthermore, these measuring devices do not predict user’s health condition after health data is obtained. Therefore, this
project aims to develop an improved health monitoring system. The system consists of multiple sensors. The system utilized heart rate click module to capture heart rate and oxygen saturation level, and TMP007 sensor to obtain body temperature. These sensors are integrated into Arduino microcontroller for data acquisition. The data will then be sent to Raspberry Pi 3 via serial communication. The data read by Raspberry Pi 3 will be used for health condition prediction through Support Vector Machine (SVM) classification model. After that, all the health informatic data and health condition will be stored in Microsoft Azure Cloud database. All the health parameter values and health condition are stored together in a table of six columns under Cloud database using MySQL query command. The mobile apps can be retrieved all data from Cloud database
using MySQL query command as well correspond to user name. To add security to the stored health data, this project has developed an Android based app having face recognition login system for the users to view their health data. The SVM classification model achieved an overall accuracy of 93.33% from 60 testing data meanwhile FaceNet classification model for face recognition achieved an overall accuracy of 99.0%. Both
classification models are accepted to be used for this project. |
| first_indexed | 2025-11-15T18:36:07Z |
| format | Monograph |
| id | usm-53382 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:36:07Z |
| publishDate | 2018 |
| publisher | Universiti Sains Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-533822022-07-15T03:35:35Z http://eprints.usm.my/53382/ Development Of Android Based Health Monitoring System Ooi, Yoong Khang T Technology TK Electrical Engineering. Electronics. Nuclear Engineering The large number of population within the age between 25 to 29 implied the importance of health care services to maintain their wellbeing. However, a lot of health measuring devices are only able to measure one health parameter. Besides, most of the measuring devices stored only a limited number of records. Furthermore, these measuring devices do not predict user’s health condition after health data is obtained. Therefore, this project aims to develop an improved health monitoring system. The system consists of multiple sensors. The system utilized heart rate click module to capture heart rate and oxygen saturation level, and TMP007 sensor to obtain body temperature. These sensors are integrated into Arduino microcontroller for data acquisition. The data will then be sent to Raspberry Pi 3 via serial communication. The data read by Raspberry Pi 3 will be used for health condition prediction through Support Vector Machine (SVM) classification model. After that, all the health informatic data and health condition will be stored in Microsoft Azure Cloud database. All the health parameter values and health condition are stored together in a table of six columns under Cloud database using MySQL query command. The mobile apps can be retrieved all data from Cloud database using MySQL query command as well correspond to user name. To add security to the stored health data, this project has developed an Android based app having face recognition login system for the users to view their health data. The SVM classification model achieved an overall accuracy of 93.33% from 60 testing data meanwhile FaceNet classification model for face recognition achieved an overall accuracy of 99.0%. Both classification models are accepted to be used for this project. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53382/1/Development%20Of%20Android%20Based%20Health%20Monitoring%20System_Ooi%20Yoong%20Khang_E3_2018.pdf Ooi, Yoong Khang (2018) Development Of Android Based Health Monitoring System. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted) |
| spellingShingle | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Ooi, Yoong Khang Development Of Android Based Health Monitoring System |
| title | Development Of Android Based Health Monitoring System |
| title_full | Development Of Android Based Health Monitoring System |
| title_fullStr | Development Of Android Based Health Monitoring System |
| title_full_unstemmed | Development Of Android Based Health Monitoring System |
| title_short | Development Of Android Based Health Monitoring System |
| title_sort | development of android based health monitoring system |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | http://eprints.usm.my/53382/ http://eprints.usm.my/53382/1/Development%20Of%20Android%20Based%20Health%20Monitoring%20System_Ooi%20Yoong%20Khang_E3_2018.pdf |