The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the d...The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.展开更多
Objective:The institutionalization of care for patients with dementia is becoming a trend.Understanding the burden on employed caregivers and exploring associated factors are of great importance in practice.Therefore,...Objective:The institutionalization of care for patients with dementia is becoming a trend.Understanding the burden on employed caregivers and exploring associated factors are of great importance in practice.Therefore,this study aimed to examine the relationship between basic attributes,caring ability,and caregiver burden in employed caregivers practicing in nursing homes.Methods:This cross-sectional study included 541 employed caregivers in 11 four-star nursing homes in Zhejiang Province from April to December 2022.Caregiver burden was assessed using the Zarit Burden Interview(ZBI).Demographic characteristics of participants,characteristics of the older patients with dementia,caring characteristics,training in dementia care,and caring abilities were collected for analysis of influencing factors.A hierarchical multiple regression analysis was conducted to explore the factors influencing the burden on employed caregivers in nursing homes.Results:The ZBI score of employed caregivers in nursing homes was 40.42±10.18,representing a moderate caregiver burden.Factors such as age(U=27.82,P<0.001),residence(U=7.89,P<0.001),educational level(H=55.81,P<0.001),self-care of older patients with dementia(H=85.21,P<0.001),daily care hours(H=73.25,P<0.001),number of older people with dementia cared for(H=14.56,P<0.012)and training in dementia care(U=-9.43,P<0.001)were significantly associated with caregiver burden.Caring ability was negatively associated with caregiver burden(r=-0.22,P<0.01).Furthermore,after controlling for demographic characteristics,the characteristics of older people with dementia,caring characteristics,training in dementia care,and caring ability explained 8.5%,5.8%,and 4.8%of the caregiver burden,respectively.Conclusion:The burden of employed caregivers on patients with dementia in nursing homes can be attributed to various factors.We recommend tailored interventions,such as dementia care training and reviewing the number and duration of hours worked to reduce the burden experienced by caregivers.展开更多
Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods...Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods:This study employed qualitative exploratory design through semi-structured individual in-depth interviews with the public pharmacists involved in the delivery of geriatric HMR at public hospitals.The purpose of the interviews was to explore challenges faced by them in the delivery of geriatric HMR.Results:Based on the emerging themes from the qualitative data,the study reveals that geriatric HMR in Malaysia is integrated as part of multidisciplinary home care visits,encompassing a diverse patient population with various healthcare needs.However,it faces challenges such as the lack of outcome monitoring,formal training,and workforce constraints.Despite these hurdles,there is a pressing need for the expansion of this service to better serve the community,and collaboration with community pharmacists holds potential to broaden its scope.Ultimately,the findings suggest that pharmacist-led HMR is both warranted and feasible within the Malaysian healthcare context.In order to optimize medicine-use among older people living in the community,approaches for expanding geriatric HMR services in Malaysia must be developed.Conclusions:This study holds profound implications as it attempts to illuminate policy makers in developing countries,enabling them to formulate effective HMR plans.By considering the challenges highlighted within this research,policy makers can design a comprehensive HMR service that caters adeptly to the healthcare needs of the mass population.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
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.
基金supported by the Department of Science and Technology of Zhejiang Province(LGF22H250002)the Health Commission of Zhejiang Province(2024KY002 to L.C.,2024KY617 to L.W.,2022KY004 to J.B.)The views expressed are those of the authors and not necessarily those of the funders.
文摘Objective:The institutionalization of care for patients with dementia is becoming a trend.Understanding the burden on employed caregivers and exploring associated factors are of great importance in practice.Therefore,this study aimed to examine the relationship between basic attributes,caring ability,and caregiver burden in employed caregivers practicing in nursing homes.Methods:This cross-sectional study included 541 employed caregivers in 11 four-star nursing homes in Zhejiang Province from April to December 2022.Caregiver burden was assessed using the Zarit Burden Interview(ZBI).Demographic characteristics of participants,characteristics of the older patients with dementia,caring characteristics,training in dementia care,and caring abilities were collected for analysis of influencing factors.A hierarchical multiple regression analysis was conducted to explore the factors influencing the burden on employed caregivers in nursing homes.Results:The ZBI score of employed caregivers in nursing homes was 40.42±10.18,representing a moderate caregiver burden.Factors such as age(U=27.82,P<0.001),residence(U=7.89,P<0.001),educational level(H=55.81,P<0.001),self-care of older patients with dementia(H=85.21,P<0.001),daily care hours(H=73.25,P<0.001),number of older people with dementia cared for(H=14.56,P<0.012)and training in dementia care(U=-9.43,P<0.001)were significantly associated with caregiver burden.Caring ability was negatively associated with caregiver burden(r=-0.22,P<0.01).Furthermore,after controlling for demographic characteristics,the characteristics of older people with dementia,caring characteristics,training in dementia care,and caring ability explained 8.5%,5.8%,and 4.8%of the caregiver burden,respectively.Conclusion:The burden of employed caregivers on patients with dementia in nursing homes can be attributed to various factors.We recommend tailored interventions,such as dementia care training and reviewing the number and duration of hours worked to reduce the burden experienced by caregivers.
基金funded by the Taylor’s University Flagship Research Grant(TUFR/2017/002/03).
文摘Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods:This study employed qualitative exploratory design through semi-structured individual in-depth interviews with the public pharmacists involved in the delivery of geriatric HMR at public hospitals.The purpose of the interviews was to explore challenges faced by them in the delivery of geriatric HMR.Results:Based on the emerging themes from the qualitative data,the study reveals that geriatric HMR in Malaysia is integrated as part of multidisciplinary home care visits,encompassing a diverse patient population with various healthcare needs.However,it faces challenges such as the lack of outcome monitoring,formal training,and workforce constraints.Despite these hurdles,there is a pressing need for the expansion of this service to better serve the community,and collaboration with community pharmacists holds potential to broaden its scope.Ultimately,the findings suggest that pharmacist-led HMR is both warranted and feasible within the Malaysian healthcare context.In order to optimize medicine-use among older people living in the community,approaches for expanding geriatric HMR services in Malaysia must be developed.Conclusions:This study holds profound implications as it attempts to illuminate policy makers in developing countries,enabling them to formulate effective HMR plans.By considering the challenges highlighted within this research,policy makers can design a comprehensive HMR service that caters adeptly to the healthcare needs of the mass population.
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.
