论文标题
Emosens：基于传感器数据分析的情绪识别，使用LightGBM
EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM
论文作者
论文摘要
聪明的可穿戴设备在我们的日常生活中发挥了不可或缺的作用。从录制ECG信号到分析体内脂肪组成，智能可穿戴设备都可以做到这一点。智能设备涵盖了各种传感器，这些传感器可用于获取有关用户身体和心理状况的有意义的信息。我们的方法着重于采用此类传感器来通过使用监督的机器学习技术在给定实例的用户心情上识别和获得差异。该研究检查了各种监督学习模型的性能，例如决策树，随机森林，XGBoost，LightGBM。借助我们提出的模型，我们使用XGBoost和LightGBM获得了92.5％的高识别率，用于9种不同的情绪类别。通过利用这一点，我们旨在即兴创造并提出方法，以帮助情绪识别，以更好地进行心理健康分析和情绪监测。
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.
