Smart Alarm Research Articles

A precise GSM and GPS integrated fire alarm system

This paper presents the idea of a fire-alarm system that is based on Global System for Mobile Communication (GSM) and Global Positioning System (GPS). Keeping in view the importance of real-time notifications for safety related issues, we developed a fire-alarm system assisted by GSM and GPS technology. It is challenging to monitor homes at far off places. However, the availability of modern sensors and wireless technology have played an important role to deal with such issues efficiently. Moreover, recent improvements in the field of information technology have provided cost effective and efficient solutions. In this study, a low-cost smart fire alarm system is designed and implemented using Arduino microcontroller that continuously receives temperature and humidity signals form the sensors. In case of any emergency, the owner receives warning messages by GSM with the exact location of incident traced by GPS. To get accurate and improved results, a variable threshold alert algorithm is used for temperature compensation and accuracy.

Reassembling the elderly consumption ensemble: retaining independence through smart assisted living technologies

ABSTRACT Smart assisted living technologies are often touted as the solution to the challenges associated with an ageing population. Viewing elderly consumers, their relatives, and technologies as comprising an assemblage, this article aims to understand how smart objects actively reshape the everyday practices in families with elderly consumers. Interviews with and observations of users of smart alarm systems indicate a stratification of the paired experiences of users and systems and identify a tension between enabling experiences of the elderly and constraining experiences of the relatives. This article contributes to views of families with elderly as assemblages by providing insights into joint and disjoint consumer experiences in multiple consumer-object assemblages, identity negotiations of the elderly and their relatives, and the hidden costs of smart assisted living technologies.

Reconfigurable 4D Printing of Reprocessable and Mechanically Strong Polythiourethane Covalent Adaptable Networks

Abstract4D printing has attracted tremendous interest because of its potential applications in smart devices, biomedical and tissue engineering. However, conventional shape memory polymers suffer from the single permanent shape and recovery direction, the flexibility of 4D printing is significantly limited. Besides, the cross‐linked networks of photocuring 3D‐printed objects cannot be reprocessed or repaired. To address these issues, the dynamic thiocarbamate bonds are introduced into the photocurable methacrylate to prepare reprocessable and self‐healable 4D printing polythiourethane (4DP‐PTU) with Young's modulus of 1.2 GPa and tensile strength of 61.9 MPa. The printed objects can be easily repaired by reprinting on the damaged surface. The shape memorized 4DP‐PTU features high shape fixity and shape recovery, and reconfigurable permanent shape brought by the solid‐state plasticity. A dual‐mode triggered alarm is obtained by the incorporation of carbon nanotubes to demonstrate the potential application in smart alarms for warning of laser exposure or fire case. Moreover, the surface wettability and cell adhesion performance of 4DP‐PTU with excellent biocompatibility can be facilely adjusted through the exchange reaction with sulfhydryl compounds. Accordingly, 4DP‐PTU may show vast potential applications in the field of robotics, smart alarm, bio‐implants and in solving the environmental challenges of 3D‐printed products.

0203 Rise and shine! The efficacy of a multimodal environmental alarm system on mitigating the effects of sleep inertia

Abstract Introduction Sleep inertia is a temporary period of reduced alertness and impaired physical and cognitive performance that immediately follows waking. Sleep inertia can have devastating consequences necessitating an intervention to successfully mitigate symptoms. Previous work has demonstrated modest benefits for individual environmental interventions which manipulate either lighting, sound, or temperature. The current study sought to expand on previous work and measure the impact of a multimodal intervention that collectively manipulated light, sound, and ambient temperature on vigilance, mood, and sleepiness. Methods 37 adults (M=27.13 years, 19 F) who self-reported taking longer than 30 minutes to wake up for 60% of their work week slept in the lab for four nights. They were woken up each morning with either a traditional alarm sound or the multimodal intervention (two control nights and two intervention nights, counterbalanced across participants). Feelings of sleep inertia were measured each morning through completion of the Psychomotor Vigilance Test and ratings of sleepiness and mood at five different time-points (5, 15, 30, 60, and 90 min after wake). Results While there was little impact of the intervention on all outcome measures, there were differential impacts depending on a person’s chronotype and the length of the lighting exposure during the intervention condition. Moderate evening-types who received a shorter lighting exposure (£15 min) demonstrated more vigilance lapses (p = 0.04) relative to the control condition whereas intermediate-types demonstrated better response speed (p < 0.005) and fewer lapses (p = 0.002). Conversely, moderate evening-types who experienced a longer light exposure (>15 min) during the intervention exhibited fewer false alarms over time (p = 0.03). Participants who received a longer light exposure also reported marginally lower negative affect the longer they were awake (p = 0.06). Conclusion Collectively, the results suggest that the length of the environmental intervention may play a role in mitigating feelings of sleep inertia, particularly for groups who may exhibit stronger feelings of sleep inertia including evening-types. Results may help inform the efficacy of “smart alarms” that activate based on entering light sleep. Future studies should measure this impact using additional measures of cognitive performance. Support (If Any)

Superelastic and responsive anisotropic silica nanofiber/polyvinylpyrrolidone/MXene hybrid aerogels for efficient thermal insulation and overheating alarm applications

Although silica aerogels with ultralow thermal conductivity are competitive for thermal insulation, their deficiencies in mechanical properties and single function affect their wide applications. Herein, we demonstrate a “soft fiber-stiff sheet” synergistic strategy for fabricating superelastic and responsive anisotropic silica nanofiber/polyvinylpyrrolidone/MXene hybrid aerogels by electrospinning, calcination, and directional-freezing. The anisotropic hybrid aerogel exhibits satisfactory thermal insulation performances at both high and low temperatures, delivering a low thermal conductivity of 21 mW m−1 K−1. The top surface temperature of the hybrid aerogel maintains at ∼74 °C after its standing on a hot plate of 300 °C. By putting the hybrid aerogel on a cold plate of −30 °C, its surface temperature is only 7.2 °C lower than the ambient temperature. Interestingly, the oxidation of MXene at high temperatures could transform the hybrid aerogel from electrically conductive to insulating, leading to sharp changes in electrical signals to trigger overheating alarm. Furthermore, the hybrid aerogel exhibits high compressibility and fatigue resistance with a high stress retention rate of 97.3% after 100 cycles under the compressive strain of 50%. Such compressive and thermally insulating aerogels are promising thermal management materials for smart alarms, and heat and cold protection of electronic devices.

A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios

A Decision Support System (DSS) is a highly efficient concept for managing complex objects in nature or human-made phenomena. The main purpose of the present study is related to designing and implementation of real-time monitoring, prediction, and control system for flood disaster management as a DSS. Likewise, the problem of statement in the research is correlated to implementation of a system for different climates of Iran as a unique flood control system. For the first time, this study coupled hydrological data mining, Machine Learning (ML), and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. Likewise, it created the platform for conditional management of floods in Iran's different clusters of climates. According to the KMeans clustering system, which determines homogeneity of the hydrology of a specific region, Iran's rainfall is heterogeneous with 0.61 score, which is approved high efficiency of clustering in a vast country such as Iran with four seasons and different climates. In contrast, the relation of rainfall and flood disaster is evaluated by Nearest Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian Process Classifier (GPC), and Neural Network (NN) algorithms which have an acceptable correlation coefficient with a mean of 0.7. The machine learning outputs demonstrated that based on valid data existence problems in developing countries, just with verified precipitation records, the flood disaster can be estimated with high efficiency. In the following, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) technique ranked the preventive flood damages strategies through three social (Se 1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of flood disaster management are collected from literature review, and the opinion approves them of 9 senior experts who are retired from a high level of water resource management positions of Iran. The outcomes of the TOPSIS method proved that National announcement for public-institutional participation for rapid response and funding (G1-2), Establishment of delay structures to increase flood focus time to give the animals in the ecosystem the opportunity to escape to the upstream points and to preserve the habitat (G 2–8), and Granting free national financial resources by government agencies in order to rebuild sensitive infrastructure such as railways, hospitals, schools, etc. to the provincial treasury (G3-10) are selected as the best solution of flood management in Social, Environmental, and Economic crises, respectively. Finally, the collected data are categorized in Social, Environmental, and Economic aspects as three dimensions of Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 experts in the five provinces of present case studies.

Complying with the GDPR when vulnerable people use smart devices

Abstract • The number of smart home devices is increasing. They are used by vulnerable people regardless of whether they are designed specifically for them or for the general population (eg, smart door locks, smart alarms, or voice assistants). • This article focuses on children and inherently vulnerable adults, and analyses how to comply with the General Data Protection Regulation (GDPR) when the latter use smart products, with a particular focus on the UK through references made to the Information Commissioner’s Office guidelines and reports. • Complying with the GDPR provisions related to the processing of vulnerable people’s data would be beneficial not only for the latter but also for organizations developing and deploying smart devices. • This article argues in favour of protecting vulnerable people’s data by design and default in every smart product. • The objective of this work is also to draw attention to the need of thinking about vulnerability across all data protection principles and to propose solutions on how to effectively comply with the GDPR in this context.

A lightweight fog-based internal intrusion detection system for smart environments

We introduce a lightweight specification-based internal intrusion detection system for smart environments monitoring IoT devices directly in fog computing layer, solving latency issues and being able to protect fog and above layers from denial of service (DoS) attacks. We further investigate different approaches to mitigate an attack considering not only fog computing layer, but also exploiting the distributed nature of IoT using mist computing layer to mitigate an attack. Considering a smart condominium fire alarm system with 300 IoT devices traffic, our approach could spot a DoS attack that comes from an infected IoT layer node and reduce the fog device processing consumption from 31%, when under attack, to 24% with fog-only acting and 9% with fog and mist layer acting afterwards.

Impact of alarm management and automation on abnormal operations: A human-in-the-loop simulation study

Process control consists of complex states of performance that require a significant amount of operator attention and skill to manage. Automation and smart alarms can enhance these processes. This study investigated the effects of alarm management and automation on process control operator workload (NASA-TLX) and performance via material lost through flaring outcomes. Eleven console qualified operators participated in a human-in-the-loop, high-fidelity simulation-based training exercise. Three levels of alarm management (no rationalization, rationalization, and state-based smart alarm) and two levels of automation (no automation and automation) conditions were implemented using six scenarios. A repeated measures two-way MANOVA indicated that both alarm management schema and automation significantly affected operator workload and performance. These results indicate that state-based smart alarm management and automation schemas may assist operators in reducing workload and material lost through flare release during abnormal operating conditions.

Continuous vital sign monitoring using a wearable patch sensor in obese patients: a validation study in a clinical setting.

Our aim was to determine the agreement of heart rate (HR) and respiratory rate (RR) measurements by the Philips Biosensor with a reference monitor (General Electric Carescape B650) in severely obese patients during and after bariatric surgery. Additionally, sensor reliability was assessed. Ninety-four severely obese patients were monitored with both the Biosensor and reference monitor during and after bariatric surgery. Agreement was defined as the mean absolute difference between both monitoring devices. Bland Altman plots and Clarke Error Grid analysis (CEG) were used to visualise differences. Sensor reliability was reflected by the amount, duration and causes of data loss. The mean absolute difference for HR was 1.26 beats per minute (bpm) (SD 0.84) during surgery and 1.84bpm (SD 1.22) during recovery, and never exceeded the 8bpm limit of agreement. The mean absolute difference for RR was 1.78 breaths per minute (brpm) (SD 1.90) during surgery and 4.24 brpm (SD 2.75) during recovery. The Biosensor's RR measurements exceeded the 2 brpm limit of agreement in 58% of the compared measurements. Averaging 15min of measurements for both devices improved agreement. CEG showed that 99% of averaged RR measurements resulted in adequate treatment. Data loss was limited to 4.5% of the total duration of measurements for RR. No clear causes for data loss were found. The Biosensor is suitable for remote monitoring of HR, but not RR in morbidly obese patients. Future research should focus on improving RR measurements, the interpretation of continuous data, and development of smart alarm systems.

Smart fire monitoring system for a city: design and development

All Fire Alarm Systems essentially operate on the same fundamental principle and framework. It provides audible and visual alarm signals. An alarm is raised if a sensor detects smoke or heat, warns people that there may be a fire, and evacuate the premises immediately. Consequently, concerned individuals may call the fire department to mitigate the emergency at which, at this point of time, the firefighter's Emergency Respond Time (ERT) is vital. ERT is one of the contributing factors to how much fire will consume lives and, or properties. In worst cases, an entire building turns into ashes, lives taken because of a slow ERT. Then there are these casual "false-alarms" caused by the false-positive readings of the installed Fire Alarm Systems. These false alarms cause mass panic and consume firefighters' valuable time and resources. The instances mentioned above are a few reasons why there is a need to implement an efficient city-wide fire alarm system.
 This study focused on the design, development and testing of a wide-area Smart Fire Monitoring System comprised of major parts, the devices and the system software. Specifically, this study aimed to develop a smart fire alarm device using the Agile Prototyping Methodology (APM) and employ Agile Software Development Methodology (ASDM) for the development of the Smart Fire Monitoring System Software (SFMSS). For the development of the smart fire alarm device, the proponents assembled necessary sensors, electrical components, microcontrollers, and other electrical modules to create a working smart fire alarm device that abled to detect smoke; detect temperature spikes; detect the existence of fire; broadcast a GPS coordinates to the fire department; transmit and receive data through RF signals using long-range radio frequency (RF) module and SMS technology; and, be functional and operational under Low Power mode. Concomitant with this device is its system software, SFMSS, to manage and process the broadcasted data. SFMSS is a centralized system developed for the fire department to monitor the city for a fire outbreak. SFMSS continuously communicates with the fire alarm devices to autonomously monitor fire presence via radio frequency and process SMS notifications containing GPS coordinates, convert them into a readable address, and plot it in a city map in the event of a fire emergency. The proponents conducted a Stress and Reliability Test on the smart fire alarm device and Product Evaluation for Quality, Efficiency, and Usability with the Bureau of Fire Protection Region 6 for the entirety of the system.

Enhanced Kevlar-based triboelectric nanogenerator with anti-impact and sensing performance towards wireless alarm system

An enhanced Kevlar-based triboelectric nanogenerator (EK-TENG) with excellent safeguarding and stable sensing capability in harsh loading environments was developed by integrating shear thickening materials and graphene on Kevlar fabric. EK-TENG, whose maximum peak power density reached as high as 25.8 mW/m2 under oscillator loadings of 40 N and 10 Hz, could directly power commercial LEDs, capacitors and supercapacitors. In addition, the 30-layer EK-TENG with the greatest fiber friction dissipated drop hammer impact force from 1820 N to 439 N under low-speed impact. EK-TENG with anti-ballistic property enabled to resist 126.6 m/s bullet shooting which was higher than 90.1 m/s of neat Kevlar. Besides, EK-TENG effectively absorbed and dissipated 87.4% of the explosion wave energy under blast loading which exhibited excellent safeguarding properties. Furthermore, EK-TENG could generate voltage signals under various impact loadings which could act as self-powered sensor to monitor external stimuli. Finally, a smart TENG-based wireless passive sensor alarm system with high sensitivity was designed to monitor and warn impact dangers, which opened up a new avenue for the development of next generation intelligent protective clothing.

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study.

BackgroundCapnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a “smart alarm” that can alert clinicians to apneic events that are predicted to be prolonged.ObjectiveTo determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds).MethodsA secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds).ResultsA total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy.ConclusionsDecision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds.

Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness

SummaryShift workers and many other groups experience irregular sleep-wake patterns. This can induce excessive daytime sleepiness that decreases productivity and elevates the risk of accidents. However, the degree of daytime sleepiness is not correlated with standard sleep parameters like total sleep time, suggesting other factors are involved. Here, we analyze real-world sleep-wake patterns of shift workers measured with wearables by developing a computational package that simulates homeostatic sleep pressure – physiological need for sleep – and the circadian rhythm. This reveals that shift workers who align sleep-wake patterns with their circadian rhythm have lower daytime sleepiness, even if they sleep less. The alignment, quantified by the sleep parameter, circadian sleep sufficiency, can be increased by dynamically adjusting daily sleep durations according to varying bedtimes. Our computational package provides flexible and personalized real-time sleep-wake patterns for individuals to reduce their daytime sleepiness and could be used with wearables to develop smart alarms.

Facile preparation of BP-MoS2/GO composite films with excellent flame retardancy and ultrasensitive response for smart fire alarm

Design and exploitation of smart fire-warning devices with ultrasensitive response capability are always an urgent need for effective fire protection. Herein, a nano-filler (BP-MoS2) is prepared by ball milling black phosphorene and molybdenum disulfide and added into the graphene oxide (GO) film to fabricate a multifunctional film with high fire resistance and rapid response ability. When the addition of BP-MoS2 is 20.0 wt%, the GO/BP-MoS2 20.0 shows an excellent flame retardancy. The residual char rate is improved from 46.60% to 66.65%, and its peak of heat release rate (pHRR) value has reduced from 599.92 W g−1 to 274.00 W g−1, resulting in a 54.33% decrease, which is attributed to the ultra-fast catalytic char formation and synergistic flame retardancy of BP and MoS2. Meanwhile, the composite film can still maintain the original shape within 15 s of ignition. More interestingly, when encountering a flame attack, it reveals an ultra-fast fire warning response (about 1 s), which is attributed to the fast carbonization after contacting with flame. Such approach offers a novel perspective for the preparation of intelligent fire alarm devices with ultra-fast response ability.

Temperature-induced resistance transition behaviors of melamine sponge composites wrapped with different graphene oxide derivatives

Temperature-responsive resistance transition behaviors of the melamine sponges wrapped with different graphene oxide derivatives (i.e. nanoribbon, wide-ribbon and sheet) were investigated. Melamine sponge composites coated by three types of GO derivatives were prepared by a simple dip-coating approach. All these composites show good mechanical flexibility and reliability (almost unchanged compressive stress at 70 % strain after 100 cycles), high hydrophobicity (water contact angle >120°), excellent flame resistance (self-extinguishing) and structural stability even after burning, which was used to construct the resistance-based fire alarm/warning sensor. Notably, the different resistance response behaviors of such sensors are strongly dependent on the GO size and network formed on the MF skeleton surface. Typically, at a fixed high temperature of ∼350 ℃, the three fire alarm sensors show different response time (to trigger the alarm light) of 6.3, 8.4 and 11.1 s for nanoribbon, wide-ribbon and sheet at the same concentration, respectively. The structural observation and chemical analysis demonstrated that the discrepancy of temperature-responsive resistance transition behaviors of various GO derivatives was strongly determined by their different thermal reduction degrees during the high-temperature or flame treating process. This work offers a design and development for construction of smart fire alarm device for potential fire prevention and safety applications.

Smart Alarm Traffic Management System for the Major Road to Isolation Hospital during the Covid-19 Pandemic in Port Said City

Transportation is an important feature that affects the quality of life. Huge increase in population, modernization in all aspects of life, and cities expansion lead to a more congested traffic that may be acceptable for in-emerging trips but enormous for emergency trips, especially for COVID 19 patients with severe respiratory symptoms. Smart transportation techniques offer solutions to the congestion problemsfor different modes of transportation and traffic management. In this paper, a smart traffic solution to the congestion problem in the major road to isolation hospital in Port Said City is presented.

Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling.

Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K-nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated thatthe LR and RF algorithms have produced the most accurate modelsfor deep and shallowwells respectively.Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.

Temperature measurement and identity detection system based on multiple embedded control units and LBP detection algorithm

This paper designs a simple non-contact temperature measurement and identity recognition device based on multiple embedded control systems and feature recognition algorithms. The device can achieve multiple functions such as non-contact temperature measurement, face recognition, mask recognition, and smart alarm. The system consists of three parts: main control, interactive system and detection system: the main control selects STM32F407VGT6 to process the data returned by multiple sensors and realize the mutual communication of each system; the interactive system uses the HMI serial touch screen to realize the visualization of data and human Machine operation function; the detection system is equipped with MLX90614 temperature detection module and OpenMV4 machine vision module to realize functions such as temperature detection, face recognition and mask recognition[1]. In addition, in order to ensure the accuracy and stability of the detection results, this article specifically designs temperature data filtering and compensation algorithms, LBP feature detection algorithms and other intelligent algorithms. Through experiments, the accuracy of this device to detect 28°C-48°C is within 0.8°C, and the accuracy of identifying faces and masks is above 98%.

Design and Research of Smart Alarm Contact Bracelet

With the development of science and technology, people’s awareness of health has increased, but in life, people’s health is mostly not controlled by themselves. There are many external constraints, such as how to save themselves when people are in danger. This bracelet is for this purpose. Users only need to wear a bracelet and touch the button to connect with emergency contacts and the 110 alarm center, providing users with professional personal security services. Bracelet holders need to apply for the real-name system, which can promptly sense when people encounter difficulties, dial 110, send the location, and record when the enemy can’t find it. Incoming mobile phone calls will be sensed and the mute answer button will not be selected. He was found and recorded and sent a text message to the emergency contact. In the event of a non-human hazard, such as a fire or an earthquake, he would promptly notify the relevant departments according to the people’s situation.