Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts.
In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from
human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches
