基于NodeMCU和Home Assistant的智能家居控制系统

为了提高家居生活的舒适性和便利性,开发了一套智能家居控制系统。该系统采用节点微控制单元(NodeMCU)作为基础硬件,负责收集传感器数据并操控相关执行器件。同时,该系统利用消息队列遥测传输协议(MQTT)和WiˉFi技术构建数据传输链路。此外,该系统利用开源的Home Assistant系统建立了后台服务端,以实现数据的图形化展示。用户可通过安卓移动设备实时监控家居环境状况,并对智能设备实施控制。该系统经过严格测试,表现稳定,显著提高了家居环境的智能化程度。

Determinants of Home Deliveries by Pregnant Mothers in Lumbo Chabbobboma Zone of Gwembe District in Zambia

Background: Home deliveries is still high globally at 42% WHO 2022, due to high home deliveries, maternal death is also high at 43% globally. In sub-Sahara region home deliveries still high. Giving birth at health facilities in most of sub-Saharan African countries Zambia inclusive is still a challenge whereby more than 51% of first-time mothers give birth at home and this gives a risk of high maternal and perinatal deaths. Therefore Reducing number of home deliveries is important to improve maternal and perinatal health issues. In this study, the aim was to investigate the determinants of home deliveries by pregnant mothers in the Luumbo zone of Gwembe district, Zambia. Purpose: Access to skilled care and facilities with capacity to provide emergency and newborn care is critical to reduce maternal death. In Zambia 42% of women still deliveries from home, suggesting a persistent challenge for women to seek, reach, and receive quality maternity care. This study aimed investigate the determinants of home deliveries by pregnant mothers in Luumbo zone of Gwembe district, Zambia. Methods: The study was conducted among postnatal mothers who came for postnatal care at 6 weeks in Luumbo Chabbobboma clinic in Gwembe district southern province of Zambia. This was a descriptive cross-sectional study where a Simple random sampling technique was used to select 105 women of childbearing age who attended postnatal and had a recent delivery. Data were collected using a researcher-administered structured questionnaire to identify determinants of home deliveries in Luumbo Chabbobboma zone. Data analysis was done using SPSS computer software version 27.0. Both descriptive and inferential (chi-square test) analyses were performed and statistical significance was taken at α ≤ 0.05. Results: The results show that 46 (43.8%) respondents were in the age bracket 20 - 29 years. Of the 105 respondents included in the study, 24 (22.9%) of them delivered from home. The results show that high maternal age (p = 0.03), occupation (p = 0.024), distance to the facility (p = 0.014), means of transportation (p = 0.023), multiparity (p = 0.01), timing and number of ANC visits (p Conclusion: From this population. The major reason why women still deliver at home was long distance to the nearest facility. To reduce maternal and perinatal mortality access to health facilities by pregnant women needs to be improved. There should also be active engagement of the traditional and religious institutions in the area.

Informing policy makers in developing countries:Practices and limitations of geriatric home medication review in Malaysia-A qualitative inquiry

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.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

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.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

A Home Automation Control System Based on LabVIEW

The objective of this project is to explore the possibility of using X-10 and LabVIEW to control the device in the house. Based on the serial port communication of LabVIEW, the X-10 module can be programmed by using the X-10 commands. Through the power line, all the devices connected to the socket will be controlled. Without replacing the existing wire, it must be an easy control system for the user who has no experience in electronics or communication engineering. Actually, this is a quite practical X-10 home automation system.

Neuroarchitecture Analysis in the Mis Abuelitos Nursing Home in Cochachinche, Huanuco, Peru - A Secondary Publication

Neuroarchitecture is a set of characteristics grouped into principles that seek to generate a certain behavior when applied.These principles will vary according to the type of user for whom the design is intended.In this research,these neuroarchitectural principles are identified in the nursing home Mis Abuelitos in Cochachinche,Huánuco.The home was designed and built with notions of nature and the Andean while emphasizing the occupants are the elderly.With this purpose,qualitative research was carried out using two sequential criteria,the first was to identify what these principles are and the second was to recognize the principles within the area.The principles that are assertively used for the elderly occupants were selected.The study observation was performed with observation and photographic sheets and was analyzed with ATLAS.ti processing software.It was obtained that the three neuroarchitectural principles are present in the nursing home which are 67%of the recommended characteristics for the elderly.

Application of horticulture therapy as a health intervention for elderly people in nursing homes:a scoping review

Objective:To review the scope of interventional studies on horticultural therapy(HT)applied to elderly people in nursing institutions to support the efficient implementation of HT among this target group.Methods:In accordance with the scoping review framework proposed by the Joanna Briggs Institute(JBI),the Pub Med,JSTOR,Web of Science,CNKI,and Wanfang databases were searched.Data from the retrieved literature were summarized and analyzed.Results:In all,18 studies were included in this review.The target population groups of interventions included self-supporting elderly people,cognitively impaired elderly people,elderly people with negative emotions,and elderly people with frailty living in nursing institutions.HT interventions,including planting,craft activities,derivative activities,and outdoor viewing activities,are implemented indoors,outdoors,or in mixed settings.The most common duration of the intervention was 8 weeks,the most common frequency was once per week,and the most common session duration was 60 min.Conclusions:The measurements used in HT interventions included assessments of physical,psycho-mental,and social health;quality of life;and activity effects.Future studies should include partially dependent groups and completely dependent groups of elderly individuals,interventions that last at least 6 months,scientifically designed activity intensity and safety-guarantee plans,and outcomeevaluation indicators such as compliance and intervention benefits.

Stress perceptions of nurses serving in home healthcare services during the COVID-19 pandemic:A qualitative research

Objective:To investigate the stress perceptions of nurses serving in home healthcare services during COVID-19.Methods:This study was qualitative research with a phenomenological design.Data were collected and recorded through in-depth interviews with 6 nurses working in MuşState Hospital,Home Healthcare Services Unit using a form consisting of 12 questions on an online platform between May 2021 and July 2021.The audio recordings were transcribed by the researcher and content analysis was performed by creating codes,categories,and themes.Results:The interviews yielded 10 categories and 59 sub-codes.These codes were addressed under the theme of"COVID-19 pandemic".Under this main theme,nurses expressed the problems they experienced in issues such as stress,support mechanisms,and family and social problems during COVID-19.They mentioned that they experienced high stress in this process,as well as social isolation and negative thoughts of society about them and that they could not spare time for themselves and their families.Conclusions:Nurses working in home healthcare services frequently express negativities such as high stress,isolation from society,and increased workload.Therefore,actions should be taken to raise awareness of society on these issues,increase the number of personnel,conduct more research,and share the results with the public.

基于Home Assistant简易家电智能控制系统的设计

【目的】本研究基于开源的Home Assistan(t HA)智能家居平台,设计出门禁、照明、电视机、窗及窗帘等简易家电的智能控制系统,能有效解决当前智能家居系统存在的成本高、操作烦琐及其众多系统互联互通难的问题。【方法】该系统以AMR主控板为核心,以人脸识别、智能感应和用户界面(UI)为用户交互终端,实现人脸识别和自动感应来控制家居终端设备,也可通过手机APP、电脑界面系统来远程控制家电设备。【结果】该系统的设计方案可靠,容易设计与实现,具有成本低廉、交互性灵活和操作简便等特点。【结论】其对家电智能化管理及智能家居的推广具有一定的指导意义。

Sleeping Conditions of Older Adults with Delirium Receiving Home Care

We aimed to clarify the sleep status before delirium onset among older adults receiving home care. The sleep status of 21 participants aged ≥65 years was monitored while they slept with a sensor placed under their bedding, after ruling out insomnia and dementia. The incidence of delirium was 28.6%;delirium onset occurred within an average of 2.7 (SD = 12) days after the start of home care among those whose care environment was changed due to hospital discharge or moving. Increased interrupted sleep and activity during sleep indicated that sleep fragmentation occurred before delirium onset. In conclusion, individuals aged ≥65 years and those whose care environment has changed should be screened for delirium because the time to delirium onset is short. Further, interventions to monitor the sleep status and prevent delirium onset should be implemented from the day home care begins.

Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

The development and use of Internet of Things(IoT)devices have grown significantly in recent years.Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices.Corporations have begun to embrace the IoT concept.Identifying true and suitable devices,security faults that might be used for bad reasons,and administration of such devices are only a few of the issues that IoT,a new concept in technological progress,provides.In some ways,IoT device traffic differs from regular device traffic.Devices with particular features can be classified into categories,irrespective of their function or performance.Ever-changing and complex environments,like a smart home,demand this classification scheme.A total of 41 IoT devices were employed in this investigation.To build a multiclass classification model,IoT devices contributed 13 network traffic parameters.To further preprocess the raw data received,preprocessing techniques like Normalization and Dataset Scaling were utilized.Feature engineering techniques can extract features from the text data.A total of 117,423 feature vectors are contained in the dataset after stratification,which are used to further improve the classification model.In this study,a variety of performance indicators were employed to show the performance of the logiboosted algorithms.Logi-XGB scored 80.2%accuracy following application of the logit-boosted algorithms to the dataset for classification,whereas Logi-GBC achieved 77.8%accuracy.Meanwhile,Logi-ABC attained 80.7%accuracy.Logi-CBC,on the other hand,received the highest Accuracy score of 85.6%.The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37%each.Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Validation of LPD Scale for the Assessment of Pressure Ulcer at Home

Objectives: This study was designed to test and validate the new LPD scale in a home care setting. The specific objectives are to validate the LPD scale for subjects cared for at home;and to compare LPD to the Braden scale for internal validity. Method: This multicenter, cross-sectional study was conducted in the domestic environment of subjects cared for Home Care services from North to South of Italy. Data collection lasted 8 months, between June 2018 and September 2020, and consisted of the simultaneous compilation of the new LPD, and the Braden scale. Home Care Expert nurses could interface with the recruited subjects and/or caregivers. The parameters considered to validate the new scale were sensitivity (Se), specificity (Sp), positive predictive values (PPV), odds ratio (OR), and the area under the receiver operating characteristic (ROC) curve. Results: Of the 679 recruited subjects, 63.2% were women, and more than 50% did not have a pressure ulcer. 48.2% of the sample aged over 85 years old;69% was affected by multiple disease, and 76.6% took a lot of drugs. 91.6% of the subjects were affected by a partial or total functional dependency. Around 50% of subjects presented double incontinence, and 43% were conscious and collaborated. 85.4% of subjects lived in a healthy environment. The predictive validity parameters showed: Se 77.25%, Sp 84.04%, PPV 91.37%, and the area under the curve (AUC) 0.88% with a confidence interval (CI) 95%. These values mean a moderately accuracy of the test. Conclusions: The new LPD scale has demonstrated a good capacity for identifying the subjects at risk of pressure ulcer and had a better discriminatory power rather than Braden scale.

Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.

A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing

Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.

Harvest Year for the Homestay Industry

This year has seen big success for China’s home stay industry,as more and more people have become the owners of homestays.According to the 2023 Insight into the Homestay Industry released by tujia.com,the number of homestay owners has increased by 77%from 2019.In addition,the booking volume of homestays with the labels of ceramic art,beachcombing and planting experiences has increased by 90-230%.

The 4th Smart Home Industry Development Forum held in Shenzhen

Jointly hosted by China Electronics Standardization Institute(CESI)and Shenzhen CESI Information Technology Co.,Ltd.,the 4th Smart Home Industry Development Forum was held in Shenzhen,Guangdong province.

Pregnant Women with Diabetes under the Novel COVID-19 Pneumonia Epidemic Home Protection and Diet and Exercise Management

Under the epidemic situation of novel COVID-19 pneumonia, pregnant women belong to the susceptible population, and their physiological and psychological conditions are particularly worthy of attention. Diabetes patients during pregnancy may have a variety of complications, which can have a serious adverse impact on their own and fetal health. This article elaborates on home protection and diet and exercise guidance for pregnant women with diabetes in order to provide guidance for pregnant women with diabetes in a special period, and further prevent and control the pneumonia epidemic caused by novel COVID-19 infection in pregnant women.