IEEE J Biomed Health Inform. 2024 May;28(5):2592-2601. doi: 10.1109/JBHI.2022.3233597. Epub 2024 May 6.
Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.
近年来，心理生理计算受到了相当多的关注。由于可以远距离、无意识地获取，基于步态的情绪识别被认为是心理生理计算领域中一个很有价值的研究分支。然而，大多数现有的方法很少探索步态的时空上下文，这限制了捕捉情绪和步态之间高阶关系的能力。在本文中，我们利用一系列研究，包括心理生理计算和人工智能，提出了一个名为 EPIC 的集成情绪感知框架，它可以通过时空交互上下文找到新颖的联合拓扑结构并生成数千个合成步态。首先，我们通过计算相位滞后指数（PLI）来分析非相邻关节之间的联合耦合，从而发现身体关节之间的潜在联系。其次，为了合成更复杂和准确的步态序列，我们探索了时空约束的效果，并提出了一种新的损失函数，利用动态时间规整（DTW）算法和伪速度曲线来约束门控循环单元（GRU）的输出。最后，使用生成和真实数据的时空图卷积网络（ST-GCN）对情绪进行分类。实验结果表明，我们的方法在情绪步态数据集上的准确率达到 89.66%，优于现有方法。
