Week 10: Data (contd.)

Hello and welcome back! This week, I continued working with my gelatin hydrogel model, but I also expanded my testing to include longer-term measurements on real skin, which ended up being one of the most important steps in my project so far. My goal was to see if my device could actually detect real dehydration and rehydration patterns, not just controlled changes in a lab setup.

First, I continued testing with my standard gelatin mixture (1 tbsp gelatin to 1/4 cup water) with no covering. I recorded humidity and temperature over multiple days to track how the gelatin changed over time. As expected, the gelatin showed a slow decrease in humidity, confirming that it was gradually losing moisture through evaporation. However, even over longer periods, the humidity still did not consistently drop to my lower threshold of 40%, which I use as an indicator for dehydration.

This graph shows that while there is a downward trend, the changes are very gradual and limited, which reinforces what I observed before: gelatin is useful for controlled testing, but it does not fully replicate the dynamic changes seen in real human skin.

To better understand how my device performs in real conditions, I conducted a longer self-testing experiment on my elbow. Over a period of time, I intentionally allowed myself to become slightly dehydrated (for example, by limiting water intake and monitoring over several hours). During this period, I recorded my sensor data continuously. For the first time, I observed that my skin surface humidity decreased significantly, eventually reaching my 40% lower threshold, which triggered the dehydration warning in my code. This was a major milestone because it showed that my system could actually detect dehydration in a real-world scenario.

After that, I rehydrated by drinking water and continued recording data. As expected, my skin surface humidity began to increase again, moving back into the normal range. This created a clear pattern of dehydration followed by recovery, which was much more noticeable than anything I saw with gelatin alone.

This graph shows a much more dynamic response compared to the gelatin graph. The drop in humidity during dehydration and the increase after rehydration are both clearly visible, which suggests that my sensors are capable of detecting meaningful physiological changes.

Comparing these two sets of data helped me understand an important point: gelatin is useful for slow, controlled testing, but real skin provides the variation needed to truly validate the device. The gelatin experiments helped confirm that my sensors can detect gradual changes, while the self-testing proved that the system can respond to real dehydration conditions.

Overall, this week was a big step forward. I was able to move beyond just testing materials and actually demonstrate that my device can detect real changes in hydration status over time. Moving forward, I plan to continue combining both methods (gelatin for controlled experiments and self-testing for real-world validation) to improve the accuracy and reliability of my hydration-monitoring patch.