Development of machine learning technique for wearable vital sign monitoring device
The Wearable monitoring device, especially the non-contact wearable monitoring device is prevalent recently. It can monitor the vital signs (heart rate and respiration rate) continuously which can be useful in several situations — for example, athletes body condition monitoring and patient emerge...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/78810 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The Wearable monitoring device, especially the non-contact wearable monitoring device is
prevalent recently. It can monitor the vital signs (heart rate and respiration rate) continuously
which can be useful in several situations — for example, athletes body condition monitoring
and patient emergency alert. Most of the non-contact device embed with radar that can
perform monitoring task in the distance. Millimeter wave based Frequency Modulated Continuous
Wave (FMCW) radar can detect human motion in millimeter sense. However, numerous
outer factors can affect sensor performance. In the digital world, machine learning can adapt to
different data into different input factors and hence perform future correction. In real life, usually,
there is only real-time discrete ground truth for the machine learning training process. To
tackle the problem in real life scenarios, this work proposed Semi-supervised regression-based
vital sign learning technique with co-training and ensemble learning style. The final trained
model achieves excellent results. |
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