基地联席主任金大卫教授合作论文“TIAN: A time series Imaging Association Network for human abnormal behavior detection”在Information Fusion发表。

Information Fusion是传播信息融合领域研究和开发各个方面信息的主要工具。旨在在一个论坛内展示多传感器，多来源，多过程信息融合领域的所有发展，从而促进有助于其发展的众多学科之间的协同作用。

摘要：

In smart healthcare, human activity recognition (HAR) is an effective technology that provides personalized treatment plans to patients. Specifically, HAR technology is used to quickly detect abnormal physical conditions in patients, thereby improving medical efficiency and service quality. With the growth of the Internet of Things and widespread adoption of wearable devices, human abnormal behavior detection technology based on multi-sensor time series has shown excellent application value. Although recent deep learning methods have shown great potential for anomaly detection, they still need to fully utilize information on behavioral differences across subjects. Meanwhile, cross-modal representation of most time series has demonstrated excellent performance, but few studies have considered both the internal evolutionary and periodic features of time-series. Addressing this research gap, this paper proposes TIAN, or Time-series Imaging Association Network, for anomaly detection in HAR. TIAN first fuses multiple time-series cross-modal feature representation methods to encode time-series into images, thereby better capturing rich time-series feature information. Furthermore, the critical innovation of TIAN is that it distinguishes habit noise across subjects and facilitates the learning of invariant features of various behavioral patterns. Experimental results on the WISDM, HARTH, and LRAD datasets demonstrate that TIAN performs well compared with existing baselines. Meanwhile, the ablation results on the WISDM dataset show that removing each component in the TIAN system consistently degrades the performance. In addition, TIAN is more robust to input noise compared to other baseline models on the HARTH dataset.

关键词：Human activity recognition;Anomaly detection;Multi-sensor time series;Generative adversarial networks

论文链接：https://www.sciencedirect.com/science/article/pii/S1566253524006845

教师简介

金大卫，中南财经政法大学信息与安全工程学院院长，数字技术与现代金融学科创新引智基地联席主任，人工智能与法商研究所所长，文澜青年学者，全国计算机学会对外联络委员会理事。长期从事信息类课程教学和金融信息工程相关研究工作。主持国家社会科学基金项目、教育部人文  社科 基金项目、国家博士后基金项目、湖北省教育厅基金项目，中央高校基本科研基金项 目10余项。发表SCI、SSCI、CSCD、CSSCI等权威期刊发表论文20余篇。