EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM

Autor:	S, Gayathri, Anand, Akshat, Vijayvargiya, Astha, M, Pushpalatha, Moorthy, Vaishnavi, Kumar, Sumit, S, Harichandana B S
Rok vydání:	2022
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted and Won the "Best paper Award" in Smart Sensor, Systems and Applications Track at IEEE CONECCT 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.14640 Zobrazit plný text záznamu View this record from Arxiv