Objective:The consequences of falls in the elderly are severe,ranging from skin abrasion to hip fracture,which is very easy to cause death.Using advanced technology to develop anti-fall clothing that meets the needs o...Objective:The consequences of falls in the elderly are severe,ranging from skin abrasion to hip fracture,which is very easy to cause death.Using advanced technology to develop anti-fall clothing that meets the needs of the elderly can play a significant role in protecting the elderly.By reviewing and analyzing the existing literature on the importance of fall protection clothing in reducing falls and protecting the body of the elderly,it is hoped to explore further research that needs improvement.Methods:Guided by the preferred reporting items for systematic reviews and meta-analyses,eight related studies were identified through Web of Science,Scopus and Chinese National Knowledge Infrastructure.The research objects,approaches,material and equipment,protection principle,and survey results are extracted.Results:Two articles verified the fall detection algorithm adopted in the research through experiments,which significantly improved fall detection accuracy.Six papers found that selecting appropriate cushioning materials can effectively reduce the consequences of falls of the elderly through experimental comparative analysis.Finally,three attributes for significant design value are drawn:(1)size and fit;(2)cushioning materials;(3)wearable sensing elements.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Objective:The consequences of falls in the elderly are severe,ranging from skin abrasion to hip fracture,which is very easy to cause death.Using advanced technology to develop anti-fall clothing that meets the needs of the elderly can play a significant role in protecting the elderly.By reviewing and analyzing the existing literature on the importance of fall protection clothing in reducing falls and protecting the body of the elderly,it is hoped to explore further research that needs improvement.Methods:Guided by the preferred reporting items for systematic reviews and meta-analyses,eight related studies were identified through Web of Science,Scopus and Chinese National Knowledge Infrastructure.The research objects,approaches,material and equipment,protection principle,and survey results are extracted.Results:Two articles verified the fall detection algorithm adopted in the research through experiments,which significantly improved fall detection accuracy.Six papers found that selecting appropriate cushioning materials can effectively reduce the consequences of falls of the elderly through experimental comparative analysis.Finally,three attributes for significant design value are drawn:(1)size and fit;(2)cushioning materials;(3)wearable sensing elements.
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.
