Enhanced Online Segmentation and Performance Evaluation Method for Real-Time Activity Recognition in Smart Homes

被引:1
作者
Ramadan, Abbas [1 ]
Said, Farida [2 ]
De lamotte, Florent Frizon [1 ]
机构
[1] Univ South Brittany, Lab STICC, UMR 6285, CNRS, Lorient, France
[2] Univ South Brittany, LMBA, UMR 6205, CNRS, Lorient, France
来源
2024 IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS, BHI | 2024年
关键词
Activity of Daily Living; ADLs; IoT Data; Human Activity Recognition; Smart Home; Real-time Activity Recognition; Spatiotemporal Segmentation; Dynamic Segmentation Algorithms; SEQUENCE; ADL;
D O I
10.1109/BHI62660.2024.10913787
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a robust method for real-time recognition of Activities of Daily Living (ADLs) in smart home environments using IoT data. Our approach improves the segmentation of sensor data streams into distinct activities by leveraging IoT sensor spatiotemporal features and applies the Needleman-Wunsch method to align predicted and actual activities. Testing on the Aruba dataset achieved 83.2% accuracy, demonstrating superior performance in segmentation and activity recognition compared to existing dynamic methods. Future work will focus on developing a sensor installation simulator to enhance accuracy and reliability.
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页数:8
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