Li Fang, Zhang Dan
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2025 Jan 27;25(3):761. doi: 10.3390/s25030761.
The rapid advancement in wearable physiological measurement technology in recent years has brought affective computing closer to everyday life scenarios. Recognizing affective states in daily contexts holds significant potential for applications in human-computer interaction and psychiatry. Addressing the challenge of long-term, multi-modal physiological data in everyday settings, this study introduces a Transformer-based algorithm for affective state recognition, designed to fully exploit the temporal characteristics of signals and the interrelationships between different modalities. Utilizing the DAPPER dataset, which comprises continuous 5-day wrist-worn recordings of heart rate, skin conductance, and tri-axial acceleration from 88 subjects, our Transformer-based model achieved an average binary classification accuracy of 71.5% for self-reported positive or negative affective state sampled at random moments during daily data collection, and 60.29% and 61.55% for the five-class classification based on valence and arousal scores. The results of this study demonstrate the feasibility of applying affective state recognition based on wearable multi-modal physiological signals in everyday contexts.
近年来，可穿戴式生理测量技术的迅速发展使情感计算更贴近日常生活场景。在日常环境中识别情感状态在人机交互和精神病学应用方面具有巨大潜力。为应对日常环境中长时、多模态生理数据的挑战，本研究引入了一种基于Transformer的情感状态识别算法，旨在充分利用信号的时间特征以及不同模态之间的相互关系。利用DAPPER数据集，该数据集包含88名受试者连续5天佩戴在手腕上的心率、皮肤电导率和三轴加速度记录，我们基于Transformer的模型在日常数据收集期间随机采样的自我报告的积极或消极情感状态的二元分类中平均准确率达到71.5%，基于效价和唤醒分数的五类分类中准确率分别为60.29%和61.55%。本研究结果证明了在日常环境中应用基于可穿戴多模态生理信号的情感状态识别的可行性。
