Abstract
This paper presents an integrated biomedical processor system consists of a biomedical processor chip, MEMS-based ECG and oximetry-based Glucose alert sensors to achieve a non-invasive home diagnostic health monitoring system for diabetics. The biomedical processor integrates various computational engines such as FFT core, CAN severity algorithm classification and wireless interface. The chip targets extremely low power consumption to enable battery-powered operation for extended period of time. The sensors will provide non-invasive monitoring of ECG signals, and blood glucose levels alert at the same physical location on the human body. The unique sensor platform inevitably drives the SoC architecture to be wearable and less intrusive; thereby, providing patients with constant feedback on their critical health parameters.
| Original language | British English |
|---|---|
| Title of host publication | Proceedings of the 2013 8th International Conference on Design and Technology of Integrated Systems in Nanoscale Era, DTIS 2013 |
| Pages | 180-181 |
| Number of pages | 2 |
| DOIs | |
| State | Published - 2013 |
| Event | 2013 8th International Conference on Design and Technology of Integrated Systems in Nanoscale Era, DTIS 2013 - Abu Dhabi, United Arab Emirates Duration: 26 Mar 2013 → 28 Mar 2013 |
Publication series
| Name | Proceedings of the 2013 8th International Conference on Design and Technology of Integrated Systems in Nanoscale Era, DTIS 2013 |
|---|
Conference
| Conference | 2013 8th International Conference on Design and Technology of Integrated Systems in Nanoscale Era, DTIS 2013 |
|---|---|
| Country/Territory | United Arab Emirates |
| City | Abu Dhabi |
| Period | 26/03/13 → 28/03/13 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- CAN
- ECG
- micromachined sensor
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