Home Environment Data Research Articles

A Next-Generation Iot-Integrated Arduino System for Intelligent Home Environment Monitoring

To stay informed about home living conditions, a home environment monitoring device was developed using an Arduino platform. Utilizing the Arduino UNO hardware and an array of sensors, the device captures real-time data on indoor temperature, humidity, light levels, and the presence of combustible gases. This information is then uploaded to a monitoring platform, allowing for adjustments to the surrounding environment based on the collected data. Testing has demonstrated that the design effectively gathers and reliably uploads home environment data, meeting the requirements for remote monitoring of living conditions. The device offers several advantages, including low cost, minimal power consumption, a simple structure, and easy installation. DOI: https://doi.org/10.52783/pst.797

Multimodal machine learning for modeling infant head circumference, mothers’ milk composition, and their shared environment

Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e−05) and maternal intake of fish (p = 4.1e−03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.

The Context of Caregiving in Heart Failure: A Dyadic, Mixed Methods Analysis.

Caregiving for persons with heart failure (HF) varies based on the individual, family, and home contexts of the dyad, yet the dyadic context of HF caregiving is poorly understood. The aim of this study was to explore dyadic perspectives on the context of caregiving for persons with HF. Family caregivers and persons with HF completed surveys and semistructured interviews. Investigators also photographed caregiving areas to complement home environment data. Descriptive qualitative analysis resulted in 7 contextual domains, and each domain was rated as strength, need, or neutral. We grouped dyads by number of challenging domains of context, categorizing dyads as high (≥3 domains), moderate (1-2 domains), or minimal (0 domains) needs. Quantitative instruments included the 36-item Short Form Health Survey, ENRICHD Social Support, HF Symptom Severity, and Zarit Burden Interview. We applied the average score of each quantitative measure to the groups derived from the qualitative analysis to integrate data in a joint display. The most common strength was the dyadic relationship, and the most challenging domain was caregiving intensity. Every dyad had at least 2 domains of strengths. Of 12 dyads, high-needs dyads (n = 3) had the worst average score for 7 of 10 instruments including caregiver and patient factors. The moderate-needs dyads (n = 6) experienced the lowest caregiver social support and mental health, and the highest burden. Strengths and needs were evident in all patient-caregiver dyads with important distinctions in levels of need based on assessment of multiple contextual domains. Comprehensive dyadic and home assessments may improve understanding of unmet needs and improve intervention tailoring.

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

BackgroundAccess to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases—fueled mostly by environmental pollution and an aging population—can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD.ObjectiveIn this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD.MethodsWe collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, “the quick brown fox jumps over the lazy dog.” We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning–based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model’s output.ResultsWe achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost—a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing “ahh”) influence the model’s decision the most.ConclusionsOur model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Maternal Education and Chinese First Graders’ Performance in Language and Literacy and Math: Role of Home Environment

Research shows that maternal education influences children’s academic performance indirectly through the quality of the home environment. In this study, we tested the role of 12 variables within the home environment (e.g., eating meals together, reading for fun at home, number of children’s books, watching TV, and bedtime) to help identify those that were effected by maternal education and then in turn affected first graders’ performance in Language and Literacy and Math. We collected home environment data from 313 Chinese parents and academic performance data directly from the children’s school. From the 12 variables, path analyses identified three that were significant: the number of children’s books at home, the number of hours that the child watched TV daily, and the number of extracurricular activities. Specifically, a higher maternal education level was related to more children’s books at home and fewer hours of TV watching daily, which were related to better performance in Language and Literacy; a higher maternal education level was related to more children’s books at home and more extracurricular activities, which were then related to better performance in math. As the identified variables are all malleable, intervention strategies that make reading materials available to children at home, limit children’s exposure to excessive TV, and promote extracurricular activity involvement may benefit children’s academic performance. Interventions that focus on increasing less educated mother’s awareness of the importance of providing reading materials to children at home, regulating children’s TV time, and involving children in extracurricular activities could also be beneficial.

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology.

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Intelligent Home with Air Quality Monitoring

The emergence of the internet of things (IoT) facilitates us to connect all the devices available at home. Also, we can control and access devices and appliances remotely in real time. This article, by taking the advantages of IoT proposes an intelligent home with air quality monitoring (AQM) system that brings controlling and monitoring of devices and appliances, and monitoring of home environment together in real time. The system uses the combination of wireless communication and cloud networking, to remote control various appliances (like Fans, TV), also getting a glimpse of air quality. In order to obtain accurate, stable values for long period, industrial class sensors are used by the system for home environment monitoring. A user-friendly Android app and web portal also increase the usability of this system. Moreover, when undesirable conditions occur, the same is reflected by an android app or web portal. Eventually, home environment data analysis is done.

Nutritional Status of perinatally HIVinfected children onantiretroviral therapy from a resourcepoor rural South African community

Objective: In Sub‑Saharan Africa, millions of children are suffering from HIV and coexisting child undernutrition. Despite efforts to curb the spread of HIV through the availability of treatment and various nutritional programmes, it has been argued that undernutrition remains highly prevalent in rural areas. The objective of this study was to describe the prevalence and psychosocial factors influencing the nutritional status in the sample of rural‑based HIV‑infected children on antiretroviral therapy. Materials and Methods: Anthropometric and home environment data were collected from 152 perinatally HIV‑infected children on antiretroviral therapy who lived with their primary caregivers in a rural Eastern Cape community. Results: More than half of the sample of children had inadequate nutritional status. The prevalence of stunting particularly was high (36.2%), while 12% were underweight and only 2.7% presented with wasting. Coexisting poor quality home‑environment (P < 0.01) added to this burden. Younger age children who lived with a younger biological caregiver were found to present more with stunting than older age children (e­™2 [n = 152] = 14.79, P = 0.005), but no significant differences were observed for underweight or wasting. Conclusion: It is important in a context such as South Africa, with the double burden of HIV infection and poverty, that all efforts be directed at alleviating undernutrition. Early pediatric HIV management should not only focus on the provision of treatment but should also prioritize the quality of care of HIV‑positive children in the home to improve on their nutritional health. Key words: HIV/AIDS, nutritional status, pediatrics, resource‑poor setting, stunting, underweight, wasting.

Nutritional status of perinatally HIV-infected children on antiretroviral therapy from a resource-poor rural South African community

Objective: In Sub-Saharan Africa, millions of children are suffering from HIV and coexisting child undernutrition. Despite efforts to curb the spread of HIV through the availability of treatment and various nutritional programmes, it has been argued that undernutrition remains highly prevalent in rural areas. The objective of this study was to describe the prevalence and psychosocial factors influencing the nutritional status in the sample of rural-based HIV-infected children on antiretroviral therapy. Materials and Methods: Anthropometric and home environment data were collected from 152 perinatally HIV-infected children on antiretroviral therapy who lived with their primary caregivers in a rural Eastern Cape community. Results: More than half of the sample of children had inadequate nutritional status. The prevalence of stunting particularly was high (36.2%), while 12% were underweight and only 2.7% presented with wasting. Coexisting poor quality home-environment (P

Sleep Stage Classification Using Unsupervised Feature Learning

Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.

An efficient data gathering routing protocol in sensor networks using the integrated gateway node

In the future ubiquitous home network, sensors will collect various home environment data in the home. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is essential that the network be energy-efficient in order to maximize its lifetime. Our study, described in this paper, was divided into two classes: the primary class was to set a gateway-selection level and the secondary class was to propose an offer of a home automation using a routing technique centered on a sensor network to set a flooding level. The proposed scheme is to increase the life time of the sensor network with the integrated gateway node. The IGN increases the life time of the network to integrate the multiple gateway nodes.

A base-station centric data gathering routing protocol in sensor networks useful in home automation applications

In the future ubiquitous home network, sensors will collect various home environment data in the home. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is essential that the network be energy-efficient in order to maximize its lifetime. Our study, described in this paper, was divided into two classes: the primary class was to set a power-based level and the secondary class was to propose an offer of a home automation using a routing technique centered on a sensor network to set a flooding level. The proposed scheme divides the home into areas such as rooms and locates the WENs in each area. The WENs collect data from sensors and deliver the data to the home base station only. Sensors deliver the data to the WENs or the home base station through the sensor with the stronger power. Performance results show that the proposed scheme is more, efficient with a wireless home network.

Granulometry and the content of toxic and potentially toxic elements in vacuum-cleaner collected, indoor dusts of the city of Warsaw

Twenty-seven sites, together with 23 household dust sample sites, representing the home environment, and four public room dust sample sites, representing working environment (mainly offices) have been described in this paper. The latter were examined to obtain an approximate reference to the home environment data. All the samples were collected between May and July 1997 by a vacuum-cleaner method, in the city of Warsaw, Poland. The granulometry of the dusts was determined by their separation into seven fractions in the range 8–500 μm. The concentrations of Cr, Ni, Cu, Zn, Pb, Br and Fe in the samples were investigated in fractions 8–32, 32–63 and 63–125 μm by the EDXRF technique. The results showed higher concentrations of these elements in finer fractions (8–32 μm). The Pb content in the household dusts was found to be unexpectedly low, ranging from 120 μg g –1 for the 63–125 μm fraction, up to 210 μg g –1 for the 8–32 μm fraction. Car exhausts could not be determined clearly as the main source of Pb in the indoor household dusts due to the lack of a Pb–Br intercorrelation. In these dusts, only Cr and Zn showed a remarkably high content of 90–100 and 1020–1070 (μg g –1), respectively. In the household dusts, strong intercorrelations were present in the three analysed fractions for the metal pairs: Pb–Zn, Pb–Cu, Fe–Cr, and Cu–Cr (weaker). The working environment rooms showed a higher degree of dustiness by 300%, as compared to the dwellings. The dusts collected in the working environment rooms showed slightly higher concentrations of Ni and by 50–100% higher concentrations of: Cu, Zn, Pb, Br than the analysed household dusts.