Department of Computer Science, Faculty of Science, Main Campus, University of Calgary, Calgary, AB T2N 1N4, Canada.
Artificial Vision Laboratory, Department of Mechanical Engineering, National Taiwan University of Science and Technology, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan.
Sensors (Basel). 2022 Jan 5;22(1):403. doi: 10.3390/s22010403.
Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance.
基于运动捕捉传感器的步态情感识别是人类情感识别的一个新兴子领域。它的应用涵盖了智能家居设计、边境安全、机器人技术、虚拟现实和游戏等多个领域。近年来，一些基于深度学习的方法成功地解决了步态情感识别（GER）问题。然而，绝大多数此类方法都依赖于具有大量模型参数的深度神经网络（DNN），这导致模型过度拟合以及推理时间增加。本文通过提出一种新的轻量级双模块架构和基于 RMSprop 优化器和分层数据混洗的手工特征，为知识领域做出了贡献。该方法在从步态中正确推断人类情感方面非常有效，在爱丁堡 Locomotive Mocap 数据集上实现了微均值平均精度为 0.97。它优于所有最近的深度学习方法，同时具有最低的推理时间，每个步态样本为 16.3 毫秒。这项研究对跨越各种领域的应用是有益的，例如情感感知辅助机器人技术、自适应治疗和康复以及监控。
