Log UV radiation and weather data on an SD card to train an Edge Impulse model. Then, run the model on XIAO BLE to get informed of potential sun damage over BLE via an Android application.
Type: Social Impact
Although many of us enjoy a sunny day in our leisure time, the glaring sun can affect our health detrimentally, especially for the elderly, children, and people with light skin. Excess sun exposure can cause minor health conditions such as sunburn, dehydration, hyponatremia, heatstroke, etc. In more severe cases, excess sun exposure can engender photoaging, DNA damage, skin cancer, immunosuppression, and eye damage, such as cataracts. Therefore, it is crucial to detect sun damage risk levels so as to get prescient warnings regarding potential health risks to mitigate the brunt of inadvertent excess sun exposure.
After perusing recent research papers on UV radiation, I decided to utilize UV index (UVI), temperature, pressure, and altitude measurements denoting the amount of UV radiation from the sun so as to create a budget-friendly BLE smartwatch to forecast sun damage risk levels in the hope of prewarning the user, especially risk groups, of potential sun damage risk to avert severe health conditions related to excess sun exposure, such as immune system damage and melanoma (skin cancer).
I collected data with XIAO BLE to build and train an artificial neural network model. Then, I ran the model on XIAO BLE to forecast sun damage risk levels and transmit the detection result over BLE outdoors. Finally, I developed a complementing Android application from scratch to obtain and display the advertised information over BLE.
By applying neural network models trained on UV index, temperature, pressure, and altitude measurements in detecting sun damage risk levels outdoors, we can achieve to:
avert UV-related skin disorders,
prevent severe health problems such as photoaging, DNA damage, and immunosuppression,
reduce the chances of getting UV-related eye damage, such as cataracts.