Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition  

作　　者：Hao Liu Zhiquan Feng Qingbei Guo 

机构地区：[1]School of Information Science and Engineering,Unicersity of Jinan,Jinan 250022,China [2]Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Unicersity of Jinan,Jinan 250022,China

出　　处：《Chinese Journal of Electronics》2025年第1期309-321,共13页电子学报(英文版)

摘　　要：In contrast to the general population,behavior recognition among the elderly poses increased specificity and difficulty,rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging.Hence,this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system.The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism,innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition.By fusing four modalities,namely image,skeleton,sensor data,and audio,we further enhance the accuracy of recognition.Additionally,we introduce a novel human-robot interaction mode,where the system associates directly recognized intentions with robotic actions without explicit commands,delivering a more natural and efficient elderly assistance paradigm.This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach.Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

关 键 词：Multi-modal recognition Elderly safety monitoring Behavior analysis Robot assistance 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP391.4[自动化与计算机技术—计算机科学与技术]