融合多传感器数据提升家庭数字孪生的可靠性
Data Sensor Fusion In Digital Twin Technology For Enhanced Capabilities In A Home Environment
- 用加速度计、陀螺仪等多传感器数据融合增强识别能力
- 融合后模型准确率显著提升，尤其在真实环境中更稳定
- 适合智能家居、远程健康监测等实际应用场景
本文研究了在数字孪生技术中融合多传感器数据以增强家庭环境应用能力，应对疫情带来的挑战及经济影响。通过Wit Motion传感器采集行走、工作、坐姿、躺卧等活动数据，测量加速度、角速度和磁场信息。研究整合了信息物理系统、物联网、人工智能与机器人技术，对比了特征级融合、决策级融合与卡尔曼滤波融合方法，并结合SVM、GBoost和随机森林等机器学习模型评估性能。结果表明，传感器融合显著提升模型的准确性和可靠性，弥补单个传感器（尤其是磁力计）的缺陷。尽管理想条件下精度更高，但多源数据融合在真实场景下表现更一致、更可信，构建出可直接应用于实际的稳健系统。
原文摘要 · Abstract (English)
This paper investigates the integration of data sensor fusion in digital twin technology to bolster home environment capabilities, particularly in the context of challenges brought on by the coronavirus pandemic and its economic effects. The study underscores the crucial role of digital transformation in not just adapting to, but also mitigating disruptions during the fourth industrial revolution. Using the Wit Motion sensor, data was collected for activities such as walking, working, sitting, and lying, with sensors measuring accelerometers, gyroscopes, and magnetometers. The research integrates Cyber-physical systems, IoT, AI, and robotics to fortify digital twin capabilities. The paper compares sensor fusion methods, including feature-level fusion, decision-level fusion, and Kalman filter fusion, alongside machine learning models like SVM, GBoost, and Random Forest to assess model effectiveness. Results show that sensor fusion significantly improves the accuracy and reliability of these models, as it compensates for individual sensor weaknesses, particularly with magnetometers. Despite higher accuracy in ideal conditions, integrating data from multiple sensors ensures more consistent and reliable results in real-world settings, thereby establishing a robust system that can be confidently applied in practical scenarios.
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