该文旨在分析我国互联网智慧护理养老的主要构成,探索其存在的问题并提出相应的对策。研究方法包括检索Web of Science核心数据库和CNKI数据库智慧养老平台的研究成果。结果显示,我国智慧养老平台正处于快速发展阶段,但也存在一些问题,...该文旨在分析我国互联网智慧护理养老的主要构成,探索其存在的问题并提出相应的对策。研究方法包括检索Web of Science核心数据库和CNKI数据库智慧养老平台的研究成果。结果显示,我国智慧养老平台正处于快速发展阶段,但也存在一些问题,主要表现在平台设计不足、模块功能单一、感知模块水平较低、缺乏平台隐私标准及成本问题。针对这些问题,该文提出相应的对策,旨在为智慧养老平台的建设和优化提供参考,从而打造符合我国老年人需求的智慧养老平台。展开更多
This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It ...This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.展开更多
文摘该文旨在分析我国互联网智慧护理养老的主要构成,探索其存在的问题并提出相应的对策。研究方法包括检索Web of Science核心数据库和CNKI数据库智慧养老平台的研究成果。结果显示,我国智慧养老平台正处于快速发展阶段,但也存在一些问题,主要表现在平台设计不足、模块功能单一、感知模块水平较低、缺乏平台隐私标准及成本问题。针对这些问题,该文提出相应的对策,旨在为智慧养老平台的建设和优化提供参考,从而打造符合我国老年人需求的智慧养老平台。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the Project Number“NBU-FFR-2024-1092-04”.
文摘This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.
