EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM

Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.