论文标题
设备转移学习，用于使用卷积神经网络个性化心理压力建模
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network
论文作者
论文摘要
在现代社会中，压力日益严重，对更广泛的人口的影响比以往任何时候都更加多。压力的准确推断可能会导致个性化干预措施的可能性。但是，人们之间的个体差异限制了机器学习模型的普遍性，将情绪推断为人们的生理学，而经历相同的情绪会有很大变化。此外，收集个人情绪的大量数据集在长时间实时标记传感器数据时，很耗时且极具挑战性。我们通过利用从20名参与者的数据训练的初始基本模型中使用转移学习来开发个性化的跨域1D CNN，这些基本模型完成了受控压力源实验。通过利用嵌入在边缘计算界面中的生理传感器（HR，HRV EDA），这些界面还包含标签技术，可以收集一个可用于在eve依转移学习中的小型现实世界中的个人数据集，以改善模型个性化和交叉范围的性能。
Stress is a growing concern in modern society adversely impacting the wider population more than ever before. The accurate inference of stress may result in the possibility for personalised interventions. However, individual differences between people limits the generalisability of machine learning models to infer emotions as people's physiology when experiencing the same emotions widely varies. In addition, it is time consuming and extremely challenging to collect large datasets of individuals' emotions as it relies on users labelling sensor data in real-time for extended periods. We propose the development of a personalised, cross-domain 1D CNN by utilising transfer learning from an initial base model trained using data from 20 participants completing a controlled stressor experiment. By utilising physiological sensors (HR, HRV EDA) embedded within edge computing interfaces that additionally contain a labelling technique, it is possible to collect a small real-world personal dataset that can be used for on-device transfer learning to improve model personalisation and cross-domain performance.
