ML Approach to Approximating Ambient Light Exposure Lori Liu, Chau Vu, Brent VanZant, Jaikrish Chitrarasu, Michael Ostertag, Tajana Rosing Rongrong Liu, Chau Vu, Brent Vanzant, Jaikrish Chitrarasu, Michael Ostertag, and Tajana Rosing   To change this poster, replace our sample content with your own. Or, if you’d rather start from a clean slate, use the New Slide button on the Home tab to insert a new page, then enter your text and content in the empty placeholders. If you need more placeholders for titles, subtitles or body text, copy any of the existing placeholders, then drag the new one into place. Methodologies Introduction Methodologies Results Background:  An accurate measure of light exposure is crucial for circadian rhythm research Participants must wear cumbersome devices during experimentation Objective:  Eliminate the need for person-bound sensors Approach:  Machine learning model trained with external inputs and features Goal: Develop machine Learning model that best predicts the wearable sensor readings based on inputs from weather data, stationary sensor, GPS, and acceleration. Data consists one set of data where the wearable sensor was inside, and another with the wearable sensor outside. A mixed data set is used to test and evaluate the accuracy of our model. Materials and Features AS7262 Light Sensor  SD Card Module Data Collection Stationary Sensor placed in frequently dwelled living space Participant wears portable sensor during day-to-day activities GPS and Acceleration recorded on participant's phone Feature Extraction Raw GPS coordinates:  - Indoor vs Outdoor Indicator  - Proximity from Stationary Sensor Indicator Acceleration values:  - Motion vs Sedentary (contextualize indoor vs outdoor) - (top right) Explained variance score over the inside/outside/mixed dataset. - (bottom) One example of predicted value compared with prediction value for the inside dataset. 9V Battery AS7262 Light Sensor  Conclusion and Furture Work SD Card Module Arduino Micro We assembled one wearable sensor and one stationary sensor for data collection - We experimented with several model structures to find one to best fit. We will continue to investigate better combinations of features. - In the future, we will expand the variety of the wearable dataset to include routes outside UCSD campus and validate on the model's accuracy. (autonomous device/ algorithm/diagram) Feature Source Data Outdoor light ambience SDSC Weather Data Dark Sky API Real-time weather data with UV, solar radiation, wind, etc. Indoor light ambience Stationary Sensor 6-channel light spectrums (lux) Location GPS Phone Application Latitude and longitude coordinates Acceleration Google Science Journal App Acceleration in x/y/z axis Ground truth light exposure value Wearable Sensor (pictured above) input Arduino Micro 9V Battery Weather Stationary Perceptron Prediction MLP Random Forest Indoor vs outdoor Weather Stationary Perceptron Prediction MLP Random Forest Indoor vs outdoor MLP Acknowledgement This project is hosted by Early Research Scholar Program of Computer Science Engineering Department. We would like to express our deep gratitude to Professor. Alvarado, the program director, and Professor. Rosing, our research advisor, for patient guidance. We thank Michael Ostertag, our project supervisor, for guiding project development on a weekly basis, and Vignesh, the program assistant, for keeping our progress on schedule.  Training the Model Mixture model to predict light exposure : Multi-Layer Perceptron model for Weather Data Random Forest model for Stationary Data Indoor vs Outdoor indicator for weight distribution output Multi-Layer Perceptron