Home Environmental Monitoring Research Articles

Sign Recognition and Voice Conversion Device for Dumb

This project proposes a comprehensive hardware setup for home automation and environmental monitoring. The system includes components such as a Bluetooth module for remote control, a voice module (APR9600) for audio feedback, an LCD for display purposes, relay modules for controlling electrical devices, bulbs with holders for lighting, a CPU fan for temperature regulation, speakers for audio output, and a 5V power supply for operation. The core functionality of the system revolves around the integration of flux sensors for environmental monitoring. When flux sensors detect specific conditions, predefined audio signals are played through the voice module. These conditions correspond to six distinct signs, facilitating real-time alerts and notifications. Moreover, the system offers remote control capabilities through Bluetooth connectivity, enabling users to manage lighting and fan operations conveniently. Additionally, temperature monitoring is implemented using a DHT11 sensor, triggering the activation of the CPU fan when temperature thresholds are exceeded. Furthermore, an LCD display enhances user interaction by providing visual feedback and status updates. The project utilizes Arduino UNO as the central processing unit and incorporates 12V 1A adapters for powering specific components. Connectors and power cables are utilized for seamless integration and electrical connectivity. Overall, this project presents an integrated solution for home automation and environmental monitoring, offering flexibility, convenience, and enhanced user experience.

Methane gas sensor based on direct absorption spectroscopy and the laser self-heating effect.

In this paper, a methane detection sensor based on direct absorption spectroscopy and the self-heating effect of lasers is proposed, which abandons the traditional method of relying on a thermoelectric cooler (TEC) to ensure stable gas concentration detection. The sensor can achieve stable concentration measurement in the temperature range of -10∘ to 40°C without the need for a TEC, which greatly simplifies the structure of the sensor and reduces the cost. The results of gas concentration calibration experiments show that the sensor has a good linear correlation (R 2=0.9993). Long-term continuous detection experiments show that the sensor maintains a relative detection error between -2.667% and 4.3% over the full test temperature range. In addition, signal-to-noise ratio analysis experiments further determine that the minimum detection limit of the sensor for methane gas is 27.33p p m⋅m (1σ). Given its advantages of simple structure, low cost, high accuracy, and stability, this methane detection sensor is well suited for natural gas leakage monitoring in home environments and can also be widely used in industrial safety detection and environmental monitoring applications. This technology provides a cost-effective solution for domestic and industrial methane detection.

Design of Home Environment Monitoring System Based on a Cloud Platform for the Elderly Living Alone

The phenomenon of elderly people living alone was becoming increasingly common in the new era, so the demand for monitoring the living environment of elderly people living alone was becoming increasingly strong. The design of home environment monitoring for elderly people living alone was designed based on a cloud platform. The system mainly consists of an STM32 main control module, a temperature and humidity detection module, an Internet of Things module, a security module, a relay control module, and a user App terminal. Then the fuzzy PID algorithm was designed to remotely control indoor temperature and humidity. After practical operation verification, the system can perform local and remote monitoring of the living environment of elderly people living alone and has the characteristics of reliability and stability.

Support Vector Machine based Automatic Classification Method for IoT big Data Features

<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.</p> <p> </p>

Design and Implementation of ESP32-Based IoT Devices.

The Internet of Things (IoT) has become a transformative technology with great potential in various sectors, including home automation, industrial control, environmental monitoring, agriculture, wearables, health monitoring, and others. The growing presence of IoT devices stimulates schools and academic institutions to integrate IoT into the educational process, since IoT skills are in demand in the labor market. This paper presents educational IoT tools and technologies that simplify the design, implementation, and testing of IoT applications. The article presents the introductory IoT course that students perform initially and then presents some of the projects that they develop and implement on their own later in the project.

The Use and Teaching of Telemedicine in Allergy/Immunology Training Programs.

The use of telemedicine has greatly increased since the onset of the coronavirus disease 2019 (COVID-19) pandemic. This review discusses the types of telemedicine, current telehealth curricula in medical education, and benefits and disadvantages of incorporation of telemedicine into Allergy/Immunology training programs. The majority of Allergists/Immunologists use telemedicine in their clinical practice with leaders in graduate medical education recommending inclusion of telemedicine in training. Fellows-in-training reported that use of telemedicine in Allergy/Immunology training during the pandemic mitigated some concerns for lack of adequate clinical experience. Still, no standardized curriculum for telemedicine training in Allergy/Immunology exists, although curricula from internal medicine and primary care residencies can provide a framework for incorporation of telemedicine training into fellowship. Benefits of telemedicine in Allergy/Immunology training include enhanced immunology training, home environment monitoring, and flexibility to reduce physician burnout while disadvantages include limited physical examination skill building and lack of a standardized curriculum. As telemedicine has been widely accepted in medicine with high patient satisfaction, it is necessary to incorporate a standardized telehealth curriculum in Allergy/Immunology fellowship training, both as a tool for patient care as well as trainee education.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

A Novel Method for Anomaly Detection in the Internet of Things using Whale Optimization Algorithm

The Internet of Things (IoT) is integral to human life due to its pervasive applications in home appliances, surveillance, and environment monitoring. Resource-constrained IoT devices are easily accessible to attackers due to their direct connection to the unsafe Internet. Public access to the Internet makes IoT objects more susceptible to intrusion. As the name implies, anomaly detection systems are designed to identify anomalous traffic patterns that conventional firewalls fail to detect. Effective Intrusion Detection Systems (IDSs) design faces three major problems, including handling high dimensionality, selecting a learning algorithm, and comparing entered observations and traffic patterns using a distance or similarity measure. Considering the dynamic nature of the entities involved and the limited computing resources available, more than traditional anomaly detection approaches is required. This paper proposes a novel method based on Whale Optimization Algorithm (WOA) to detect anomalies in IoT-based networks that conventional firewall systems cannot detect. Experiments are conducted on the KDD dataset. The accuracy of the proposed method is compared for classifiers such as kNN, SVM, and DT approaches. The detection accuracy rate of the proposed method is significantly higher than that of other methods for DoS, probing, normal attacks, R2L attacks, and U2R attacks compared to other methods. This method shows an impressive increase in accuracy when detecting a wide range of malicious activities, from DoS, probing, and privilege escalation attacks, to remote-to-local and user-to-root attacks.

Intelligent Home Using Fuzzy Control Based on AIoT

The Internet of Things has grown rapidly in recent years, and the technologies related to it have been widely used in various fields. The idea of this paper is to build a set of Internet of Things systems in a smart home wireless network environment, with the purpose of providing people with a more comfortable, convenient, and safe life. In the sensing layer of the Internet of Things, we discuss the uses of common sensing technologies on the Internet and combine these with Arduino microprocessors to integrate temperature sensing modules, humidity sensing modules, gas sensing modules, and particulate matter 2.5 (PM2.5) sensing modules. In the network layer, we discuss using the Wi-Fi wireless networking function composed of a home router and a wireless Wi-Fi chip Espressif system 8266 (ESP8266) to transmit the collected home-sensing data to the ThingSpeak cloud database. Finally, in the application layer part, the system uses a mobile device with fuzzy calculation optimization software. The system is also connected remotely for home environment monitoring, so the home environment can be optimized anytime, anywhere.

Smart Home Environment Monitoring System Based on Microcontroller

More than 90% of a person’s lifetime is spent indoors in a variety of activities, thus the parameters of the home environment affect human physical and mental health. With the development of Internet of things technology, people put forward new and higher requirements for the smart home. Based on the microcontroller, in this paper, we use multiple sensor systems to monitor various indoor environmental parameters, that is, to collect and display the environmental temperature, humidity, light intensity, CO concentration, and smoke concentration accurately and in real time. In the process of monitoring, the limits of various environmental parameters can be set artificially. Different types of alarms will be activated when one or several parameters are detected to exceed the limits. The microcontroller transmits the monitoring data to mobile terminals such as smartphones and others through Bluetooth modules, and the display interface is clear and intuitive. After testing, the system has the advantages of accurate measurement results, stable performance, and simple operation, which has a certain promotion and use value in smart home environment monitoring.

Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion.

Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.

Prototyping an IoT-based system for monitoring building indoor environment

PurposeThis study aims to develop an architectural prototype of a Cyber-Physical System (CPS), as well as lay a technological foundation for future smart housing with improved health and well-being outcomes for its occupants.Design/methodology/approachThis study deploys smart sensors to monitor the key environmental parameters of a house. Using Internet of Things technology, a prototype of a CPS has been developed for capturing the environmental conditions over time. A case study involving a property in New Zealand was undertaken to validate the prototype.FindingsThe study proposes a monitoring platform, enabled by the CPS and smart sensing devices, that collects, shares, stores, analyses and visualises indoor environment data. The reliability and accuracy of the monitoring system were enhanced by comparing the activity of house occupants with sensor data.Research limitations/implicationsDue to limited time, the prototype was tested in one house for a period of one month. Air quality was not considered in this study. However, the work suggests that such an approach provides an effective solution for government organisations and housing agencies to collect information for the purpose of assessing building thermal performance.Originality/valueThis research proposes a new lens consisting of a home environment monitoring application with health and well-being implications. It could also be used to inform the future design of healthy homes and buildings, both in New Zealand and internationally.

Safety Training Monitoring System of Ski Resort Based on Wireless Sensor Network

With the gradual rise in China’s ski industry, skiing is now the most common sport among winter outdoor sports, which is closely related to the origin of safety issues. Based on the ski resort, training is the key to safety detection, only to ensure the safety of training personnel and to maintain the momentum of high-speed development of skiing sports for a long time. The monitoring system plays an important role in the wireless sensor network monitoring system. Common monitoring systems are used for agriculture, smart home, and outdoor environment monitoring systems, most of which are professional fields. Based on the safety of ski resorts, this paper puts forward the research of ski training safety system based on the wireless sensor. In the research of ski training detection system based on the wireless sensor network, first, the safety system of domestic ski resort is analyzed and detailed suggestions are given. Then, this paper studies the routing protocol of wireless communication technology in detail and compares the service life and energy consumption of nodes under the use of two different routing protocols in wireless sensor networks. According to the data experiment in this paper, LEACH-MH protocol has great advantages over the LEACH protocol. The LEACH-MH protocol detects 185 more rounds than the LEACH protocol in the total detection lifetime. In the detection of energy consumption of 100 m ∗ 100 m, the LEACH protocol consumes 50J in 400 rounds, while the LEACH-MH protocol consumes only 50J in 760 rounds.

Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments.

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life and users’ inability to remember to charge and wear the devices are often the challenges for their usage. In addition, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. Therefore, this paper proposes the use and fusion of privacy-friendly and Unobtrusive Sensing Solutions (USSs) for data collection and processing during SPAREs in home environments. The present work aims to monitor SPAREs such as dorsiflexion, plantarflexion, inversion, and eversion using radar and thermal sensors. The main contributions of this paper include (i) privacy-friendly monitoring of SPAREs in a home environment, (ii) fusion of SPAREs data from homogeneous and heterogeneous USSs, and (iii) analysis and comparison of results from single, homogeneous, and heterogeneous USSs. Experimental results indicated the advantages of using heterogeneous USSs and data fusion. Cluster-based analysis of data gleaned from the sensors indicated an average classification accuracy of 96.9% with Neural Network, AdaBoost, and Support Vector Machine, amongst others.

A Smart Mirror for Emotion Monitoring in Home Environments.

Smart mirrors are devices that can display any kind of information and can interact with the user using touch and voice commands. Different kinds of smart mirrors exist: general purpose, medical, fashion, and other task specific ones. General purpose smart mirrors are suitable for home environments but the exiting ones offer similar, limited functionalities. In this paper, we present a general-purpose smart mirror that integrates several functionalities, standard and advanced, to support users in their everyday life. Among the advanced functionalities are the capabilities of detecting a person’s emotions, the short- and long-term monitoring and analysis of the emotions, a double authentication protocol to preserve the privacy, and the integration of Alexa Skills to extend the applications of the smart mirrors. We exploit a deep learning technique to develop most of the smart functionalities. The effectiveness of the device is demonstrated by the performances of the implemented functionalities, and the evaluation in terms of its usability with real users.

Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants.

Background and Objectives: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. Materials and Methods: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. Results: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. Conclusions: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging.

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet,1 that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.