This work presents the development of a miniaturized, low-power Printed Circuit Board (PCB) able to acquire the PPG signal from the head. The board was integrated into a pair of eyeglasses and was connected to a main board which was programmed to provide continuous PPG monitoring, real-time Heart Rate (HR) estimation and on-edge Signal Quality (SQ) assessment. In the testing conditions, the board consumes up to 825 µW and proved its capability to perform continuous multiwavelength PPG measurements from the user’s nose. Preliminary measurements involving 4 subjects in controlled conditions were carried out to assess the HR estimation algorithm accuracy. Against a chest-worn Electrocardiography (ECG) sensor, the algorithm showed a maximum Mean Absolute Error (MAE) of 3 bpm at rest and 12 bpm after low-intensity physical exercise. The SQ assessment functionality tested on just one subject showed that the implemented algorithm can be confidently used in controlled settings to provide feedback regarding the PPG sensor’s optimal positioning or to calibrate the sensor’s parameters to the specific user. Although preliminary, the obtained results are promising and support the system’s usability.
Development of a Miniaturized, Low-Power, Head-Mounted PCB for Continuous PPG Monitoring and Real-Time HR Estimation
I. Crupi;A. Scandelli;A. Giudici;F. Villa
2024-01-01
Abstract
This work presents the development of a miniaturized, low-power Printed Circuit Board (PCB) able to acquire the PPG signal from the head. The board was integrated into a pair of eyeglasses and was connected to a main board which was programmed to provide continuous PPG monitoring, real-time Heart Rate (HR) estimation and on-edge Signal Quality (SQ) assessment. In the testing conditions, the board consumes up to 825 µW and proved its capability to perform continuous multiwavelength PPG measurements from the user’s nose. Preliminary measurements involving 4 subjects in controlled conditions were carried out to assess the HR estimation algorithm accuracy. Against a chest-worn Electrocardiography (ECG) sensor, the algorithm showed a maximum Mean Absolute Error (MAE) of 3 bpm at rest and 12 bpm after low-intensity physical exercise. The SQ assessment functionality tested on just one subject showed that the implemented algorithm can be confidently used in controlled settings to provide feedback regarding the PPG sensor’s optimal positioning or to calibrate the sensor’s parameters to the specific user. Although preliminary, the obtained results are promising and support the system’s usability.| File | Dimensione | Formato | |
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Development of a Miniaturized Low-Power Head-Mounted PCB for Continuous PPG Monitoring and Real-Time HR Estimation.pdf
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