High-accuracy low-precision machine learning system for health monitoring
Background The increasing rates of cardiovascular diseases (CVDs) within global and local populations is alarming. Although older adults have higher risks of developing CVDs (Rodgers et al., 2019), it is crucial for individuals of all ages to be mindful of their cardiovascular health and take preve...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162809 |
| _version_ | 1811683887951118336 |
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| author | Ang, Wei Jun |
| author2 | Mohamed M. Sabry Aly |
| author_facet | Mohamed M. Sabry Aly Ang, Wei Jun |
| author_sort | Ang, Wei Jun |
| collection | NTU |
| description | Background
The increasing rates of cardiovascular diseases (CVDs) within global and local populations is alarming. Although older adults have higher risks of developing CVDs (Rodgers et al., 2019), it is crucial for individuals of all ages to be mindful of their cardiovascular health and take preventive measures or initiate medical care if necessary. This could be done through cardiovascular health monitoring, which allows detection of early symptoms of CVD; a common one being abnormality in heart rate. A wide array of devices for cardiovascular health monitoring purposes have been developed in recent years, ranging from wireless electrocardiogram (ECG) monitors to wearable gadgets such as smartwatches. Given the broad spectrum of heart rates of individuals of different age groups and backgrounds, the accuracy of such devices in detecting heart rate abnormality is a pivotal aspect in the development of these devices.
Objective
The objective of this project is to develop a high accuracy, low-precision machine learning system to alert users when abnormalities in heart rates for various activities are detected.
Methods
A Raspberry Pi (RPI) was used as an intermediary for the Himax and smart sensor watch to communicate. Upon booting up the RPI, the smart sensor watch transmits real-time heart rate data from the user to the RPI, which was forwarded to the Himax. Accelerometer data together with the heart rate data was fed into the model for inference. In accordance with the intensity of activity conducted, when an abnormality in heart rate is detected, users will be alerted through a red light on the LED.
Results
The machine learning system is able to classify correctly with a rate of 99.46%
Recommendation
Functions such as emergency response and Bluetooth communication between the health sensor band and RPI can be implemented in the future with sufficient time and resources. |
| first_indexed | 2024-10-01T04:19:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162809 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:19:53Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1628092022-11-09T08:20:31Z High-accuracy low-precision machine learning system for health monitoring Ang, Wei Jun Mohamed M. Sabry Aly School of Computer Science and Engineering msabry@ntu.edu.sg Engineering::Computer science and engineering Background The increasing rates of cardiovascular diseases (CVDs) within global and local populations is alarming. Although older adults have higher risks of developing CVDs (Rodgers et al., 2019), it is crucial for individuals of all ages to be mindful of their cardiovascular health and take preventive measures or initiate medical care if necessary. This could be done through cardiovascular health monitoring, which allows detection of early symptoms of CVD; a common one being abnormality in heart rate. A wide array of devices for cardiovascular health monitoring purposes have been developed in recent years, ranging from wireless electrocardiogram (ECG) monitors to wearable gadgets such as smartwatches. Given the broad spectrum of heart rates of individuals of different age groups and backgrounds, the accuracy of such devices in detecting heart rate abnormality is a pivotal aspect in the development of these devices. Objective The objective of this project is to develop a high accuracy, low-precision machine learning system to alert users when abnormalities in heart rates for various activities are detected. Methods A Raspberry Pi (RPI) was used as an intermediary for the Himax and smart sensor watch to communicate. Upon booting up the RPI, the smart sensor watch transmits real-time heart rate data from the user to the RPI, which was forwarded to the Himax. Accelerometer data together with the heart rate data was fed into the model for inference. In accordance with the intensity of activity conducted, when an abnormality in heart rate is detected, users will be alerted through a red light on the LED. Results The machine learning system is able to classify correctly with a rate of 99.46% Recommendation Functions such as emergency response and Bluetooth communication between the health sensor band and RPI can be implemented in the future with sufficient time and resources. Bachelor of Engineering (Computer Engineering) 2022-11-09T08:20:31Z 2022-11-09T08:20:31Z 2022 Final Year Project (FYP) Ang, W. J. (2022). High-accuracy low-precision machine learning system for health monitoring. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162809 https://hdl.handle.net/10356/162809 en SCSE21-0849 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Ang, Wei Jun High-accuracy low-precision machine learning system for health monitoring |
| title | High-accuracy low-precision machine learning system for health monitoring |
| title_full | High-accuracy low-precision machine learning system for health monitoring |
| title_fullStr | High-accuracy low-precision machine learning system for health monitoring |
| title_full_unstemmed | High-accuracy low-precision machine learning system for health monitoring |
| title_short | High-accuracy low-precision machine learning system for health monitoring |
| title_sort | high accuracy low precision machine learning system for health monitoring |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/162809 |
| work_keys_str_mv | AT angweijun highaccuracylowprecisionmachinelearningsystemforhealthmonitoring |