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Nosocomial infection is a health issues related to the in-hospital activities. One of the indirect cause was the contamination related to the waste disposal activity. All hospital waste was deemed hazardous that it needs extra care during collection. Cross contamination may arises when the waste containers was not treated. To alleviate the problem and to promote healthy habits, this article describe a smart disinfectant sprayed waste collection container and its status reports. By automating the lid opening and closing that was triggered by human proximities using passive infrared detector and ultrasound sensor, it provide convenience in waste disposal. This also prevent cross contamination related with physical contact with the waste container which may induce the nosocomial infection. The container also tracking its remained waste capacity and will report when it was almost full to prevent overflowed and to alarmed for immediate waste pickup. In case of delayed container collection, the lids will remain closed and the light indicator turn to red. The indicator will turn green in normal conditions and turn yellow if the container was almost full. ESP32 was used to interface with ultrasound sensors to approximate the container remaining capacity. Passive infrared sensor and a servo motor was linked to actuates the lid opening and closing. Some disinfectant was sprayed for every detected increase in waste level. The disinfectant aerosol canister was pushed by a DC motor. For reporting, the systems was equipped with LoRa for status report. This systems was tested inside Poltekkes Kemenkes Jakarta II building and could reach at least 80m distance from the report center for reporting.

In this paper passive Infrared sensor “PIR” based security system is introduced. With this sensor we can save power, and effective management at low cost and requires small memory space.

The additive manufacture is a fabrication process that has taken huge steps in the last decade, even though the first researches and prototypes are around since almost forty years ago. In this article, a design method for a Parallel Kinematics Robot for Additive Manufacturing Applications is proposed. A numerical model is obtained from the kinematics of the robot for which the design, construction and assembly are planned using recycled materials and equipment. The control of the robot is done using open source software, allowing the planning of trajectories in the Cartesian space on a maximum designed cylindrical workspace of 300mm in diameter by 300mm high. At the end of the work the robot was identified, the kinematic model was validated and considerations for future works were given.

In this paper passive Infrared sensor “PIR” based security system is introduced. With this sensor we can save power, and effective management at low cost and requires small memory space.

The consumption of medicine is typical in geriatrics, having many problems related to medications. Geriatrics often forget to take their medicine, and this problem can be overcome by using an automatic reminder system. In this study, an automated reminder system is developed as an improved community element, acting as a system that can help geriatric in taking their medicine on time, thus, boosting their health condition. This reminder system also includes an interaction between the geriatrics and their caretakers. This reminder system includes Arduino UNO as the microcontroller, with the notification system, Blynk Application, a buzzer, and a light-emitting diode (LED) system. To make this reminder system more versatile, the buzzer will alarm during the medicine intake time, giving information to the elderly on which medicine to take. When the time has reached to take medication, the buzzer will produce a sound. Suppose the medicine box opens after the buzzer's sound and is detected by the passive infrared sensor (PIR sensor). In that case, the caretaker will receive a notification through the Blynk application that the geriatric already took medicine. On the contrary, if the medicine box is not open after 3 minutes following the buzzer's sound, which indicates that the geriatric did not take their medicine, the system will not send a notification to their caretakers on the status. This prototype is tested on ten users for its accuracy and effectiveness. It is believed that this system can provide geriatrics more alert in taking their medicine on time, enhancing their health status.

When using camera traps for wildlife studies, determining suitable camera models and deployment methods is essential for achieving study objectives. We aimed to determine if camera trap performance can be increased by (1) using cameras with wider detection angles, and (2) by periodically repositioning cameras within sites. We compared three camera trap groups: stationary Reconyx PC900/HC600 (40° detection angle), and paired, periodically-repositioned Reconyx PC900/HC600 and Swift 3C wide-angle camera traps (110° detection angle). Cameras operated simultaneously at 17 sites over 9 weeks within the Upper Warren region, Western Australia. Swift cameras had significantly higher detection rates, leading to better performance, especially for species 10 kg bodyweight. Reconyx cameras missed 54% of known events, with most being animals that moved within the cameras’ detection zones. Stationary and periodically-repositioned Reconyx camera traps performed similarly, although there were notable differences for some species. The better performance of Swift 3C wide-angle camera traps makes them more useful for community-level and species-level studies. The increased sensitivity of the Swift’s passive infrared sensor along with the wider detection zone played an important role in its success. When choosing camera trap models, detection angle and sensor sensitivity should be considered to produce reliable study results. Periodically repositioning cameras within sites is a technique that warrants further investigation as it may reduce camera placement bias, animal avoidance of camera traps, and increase spatial/habitat information when a limited number of cameras are deployed.

Security is of enormous importance in this modern age. The crime rate increases exponentially, and traditional home surveillance systems are expensive and limited in intelligent capabilities, such as real-time detection, instant alert, and prompt reporting. As a result of the rapid evolution of cutting technologies and devices, such as Raspberry Pi and several enabled APIs, the limitation, as mentioned, has become a thing of the past. This paper implements an Intrusion Detection System (IDS) using a Raspberry Pi processor that serves as a controller and stations at a home environment. The choice of Raspberry Pi 4 is considered, which is the core of the system; it does all processing between all components (i.e., Passive Infrared sensor, Web camera, Light Emitting Diode) connected to it. The intelligent device is integrated with the Telegram bot via its API. The API with the appropriate Telegram framework is used as an instant messenger generator and notification system. The designed system achieved promising results, as it accurately identified known people, and all unidentified people were flagged by sending a Telegram text message, which included the captured image of the unknown intruder. The overall accuracy rate achieved was 84 per cent, and the accuracy rate for known people was 86 per cent.

Electricity has the major resource consumption among many resources in the country. Also, it is the most misused resource, so control of consumption needs to be taken so that cost is maintained and future generation is benefited. The system proposed here represents a low cost and efficient classroom system which controls appliances like lights and fans using NodeMcu module and relays and adapters with the available LAN or Wi-Fi in the University, so that devices can be remotely controlled using the web. This also uses low-cost sensor which detects the presence of humans switching on the lights when necessary and off if humans are not detected. The simple and effective sensor called Passive Infrared Sensor (PIR sensor) will be used in each classroom. Human existence is sensed by the sensor through heat radiations which are produced by humans. Then the input is given as the signal through simple web application is created. The procedure to operate the devices which manually followed to operate the devices is however neglected, therefore, installing this type of equipment, and a web application will surely reduce the wastage of electricity in classrooms by remote-operating devices.

Funder: Cardiff University, Funder: Project CASE partner ‐ Eco‐explore Community Interest Company, Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.Here, we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof-especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm-petrel (Monteiro's storm-petrel Hydrobates monteiroi and Madeiran storm-petrel Hydrobates castro) during their respective breeding seasons.Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30-s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera-build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviors and interspecies interactions.

Public campus has a mandate to saving of electrical energy. Electrical energy consumption is often wasteful in building. There is tendency wasteful by user. Electronic equipment is often still turn on at idle time. Only a few students want to turn off the equipment and shut down the computer. Saving of electrical energy is not only at idle time but it can be improved into operational hour. It is not depending on idle time or operational hours, but depends on human presence. Implementation of electrical energy saving has to be supported by frugal behavior and equipment technology. In this study, we name system of smart detection and control to electrical energy (Sisdece). This system is consist of hardware and software. Hardware applies passive infrared sensor (PIR) sensor, wireless sensor network (WSN), microcontroller ESP32, access point, relay. Software use C++, hypertext preprocessor (PHP), hypertext markup language (HTML) and android studio. Result of measurement has been done in a month during November 2020. Average of energy saved is 12.51 kWh and total of electrical energy is 105.86 kWh. Comparison of energy saved to electrical energy is 11.81%. This is a significant reduction to electrical bill. The result is expected as benchmark of electrical energy management in Politeknik Negeri Medan (POLMED).

International audience; Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness–ethical considerations for human fall detection by Information and CommunicationTechnologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16×16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26–28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were “conference papers”, five were “original articles” and one was a found in arXiv.org (an open access repository of scientific research). Since four authors “duplicated” their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85–90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.

IoT makes use of individual entities that are interconnected with one another and have pre-programmed components to respond to variations. One part of smart cities is the smart home. The automation of the smart home requires all components like lights, fans, refrigerators, or any other appliances to work remotely which requires data to be sent to remote users accurately and securely. The sensors required in a smart home include a gas sensor, temperature sensor, passive infrared sensor, and some alarm system in case of emergency. All these sensors produce enormous amounts of timestamped data that needs to be sent through insecure channel internet to reach to the destined remote account that can handle the situation and can take necessary action depending on values sent by these sensors. This chapter proposes an authenticated encryption mechanism to securely communicate home automation data to the destination which can be validated and decrypted to get back the original message ensuring no tampering of data has occurred while communicating the data. For the purpose of simplicity, this chapter considers data from gas sensor, temperature sensor, buzzer, and passive infrared sensor.

In this paper passive Infrared sensor “PIR” based security system is introduced. With this sensor we can save power, and effective management at low cost and requires small memory space.

This letter describes a developed wireless sensor network based on a proposed algorithm for monitoring the environmental parameters in healthcare intentions. This proposed algorithm contains a frame with different packets that are implemented on the developed wireless sensor network. The developed wireless sensor network consists of one central node as well as four sensor node that has been equipped with various sensors such as temperature, humidity, CO, CO2, and passive infrared sensor. In order to test the presented algorithm and the developed wireless sensor network, the sensor nodes are situated in four different rooms in a hospital for recording essential parameters of the environment while the central node is put in the nurse station for warning to nurses. The obtained result of the proposed sensor nodes in comparison to gold standards shows root mean square error 1.1%, $$0.35\,^\circ \hbox {C}$$, 0.98% for humidity, temperature and gas, respectively. Also, the obtained results illustrate that the system gives accurate feedback from environmental temperature, humidity, and CO, and CO2 to the nurse station in order to increases the possibility of a healthy environment condition for patients.

Detecting count of human beings accurately in a closed indoor environment is crucial in diverse application areas including search and rescue, surveillance, customer analytics, abnormal event detection, human gait characterization, congestion analysis, and many more. Moreover, it has significant importance in preventing any intrusion in a secured indoor space such as a bank vault. Sensor-based technologies (for example camera, Passive Infrared sensor (PIR), etc.) are becoming more popular day by day to ensure enhanced security in such closed indoor setting. However, sensors used in these technologies have to be deployed in visible places. Thus, there exist possibilities of damaging the sensors by intruders. Therefore, this paper proposes a novel methodology to detect human count in such closed indoor setting, which can be deployed in any hidden place being oblivious to intruders. Here, we perform human counting based on different environmental gaseous parameters (Carbon Dioxide, Liquefied Petroleum Gas or LPG, Nitrogen Dioxide, and Sulfur Dioxide) and two weather parameters (temperature and humidity). We conduct numerous experiments under closed controlled settings and perform the counting task using different machine learning algorithms such as Bagging, RandomForest, IBK, and J48. We also propose a novel Deep Neural Network based framework for this task and evaluate its performance on the collected data sets. Using this method, we achieve 95% accuracy in detecting the number of humans present even in the unseen test case scenario.

This paper proposes an optimization-based Residential Energy Management System (REMS) to minimize not only the customer's energy cost but also customer dissatisfaction with the Power Holding Company of Nigeria (PHCN). REMS is designed in such a way that when there is a person in a particular room, electrical power is supplied to that room from its main source, and once the person leaves and the room, the power supply is cut off, shutting off all appliances and devices in that room. This is achieved by the usage of a passive infrared sensor (PIR) to detect motion and an ArduinoMicrocontrollerATMEGA-328Pto process input data from the PIR sensors to determine if electrical power is needed in a room or not. A Microcontroller PIC16F877A is also used to carry out all processing activities whose result would be effected through the aid of five output devices with a Liquid Crystal Display (LCD) used to display the PIR and the microprocessor status. The current state of the prototype design is described and a brief introduction to the control approach is made.

Passive infrared (PIR) sensors are the most popular deployed sensors in buildings for individual presence detection. However, PIR sensors are motion detectors in nature, responding only to incident radiation variation, which leads to false negative detections, inaccurate occupancy estimation, and uncomfortable lighting swings and waste of energy. To address this issue, an optical shutter driven by a Lavet motor PIR (LAMPIR) sensor is developed for true presence detection. In comparison with our previously developed chopped PIR (C-PIR) and rotationally chopped PIR (Ro-PIR) sensors, a low-power single-phase electro-mechanical driving approach is introduced for LAMPIR to replace traditional servo and stepper motors and thus significantly reduce the power consumption by up to 89%, size by up to 60%, weight by up to 75%, cost by up to 31%, and acoustic noise by 12 dBA. More specifically, driven by pulsed signal from a microcontroller unit, the electro-mechanical vibrator drives a semi-transparent long-wave infrared optical shutter to chop the field of view (FOV) of a PIR sensor periodically. By monitoring and analyzing the voltage outputs generated by the LAMPIR senor, high-accuracy presence detection can be achieved by optimizing the shutter width and shuttering period through parametric studies. Experimental results show that a classification accuracy of 100% can be reached for detecting stationary occupants up to 4.5 m and moving occupants up to 10 m, suggesting a detection range improvement from both the C-PIR and the Ro-PIR sensors (4.0 m for stationary and 8.0 m for moving occupancy detection for both sensors). Additionally, the LAMPIR sensor has an FOV of 90° in horizontal and 100° in vertical, which is sufficient for most applications. For a 31-h-long presence detection test, an accuracy of 97% is achieved when classifying unoccupied and occupied scenarios, while an accuracy of 93% is achieved when classifying unoccupied, stationary and moving occupant scenarios.

The goal of this paper is to localize a resident in indoor environments by using motion information from distributed environmental sensors and body activity information from wearable sensors. The passive infrared sensor nodes distributed in a home provide binary information about human motion in their field of views, while the wearable inertial measurement unit sensor node collects motion data that can be used in body activity recognition, walking velocity, and heading estimation. Basic human activities such as sitting, sleeping, standing, and walking are recognized. We proposed a particle filter-based sensor fusion algorithm that takes advantage of the human location/activity correlation in indoor environments to increase the localization accuracy. Experiments were conducted in a mock apartment testbed. We used the ground truth data obtained from a motion capture system to evaluate the results. Note to Practitioners —This paper is motivated by a goal to realize various home automation applications through human localization. For example, with the knowledge of human location, lighting, and air conditioning systems can be more efficiently managed, or a home service robot can provide better service to the resident. There currently lacks an accurate human localization method in indoor environments. The theoretical framework proposed in this paper aims to combine simple environmental sensors (passive infrared sensors) and a single wearable motion sensor to accurately track human locations. This method reduces the intrusiveness to the human, while keeping the cost of the infrastructure low. The theoretical framework has been tested and evaluated in a mock apartment environment as a proof of concept. For deployment into real homes, we need to conduct more realistic tests and thorough evaluation of the system. It is also necessary to reduce the size and weight of the wearable motion sensor and simplify the installation of the environmental sensors.

Crimes against property without using violence, in this case, are theft and burglary is the type of crime that is most common every year. However, home security needs a security system that is more efficient and practical. To overcome this, an internet of things (IoT) is needed. This research evaluated the performance prototype by reading distance from the radio frequency identification (RFID) reader using E-KTP and quality of service performance (i.e throughput and delay) from application android. This research design smart door lock using RFID sensor, passive infrared sensor (PIR), solenoid as door locks, buzzer, led, E-KTP as RFID tags and also android application to controlling and monitoring made with android studio is connected to NodeMCU V3 ESP8266 as storage data and connect with firebase realtime database instead of conventional keys. This research focuses on performance prototype and quality of service from features application is work well. Related to previous works, our evaluation shows that the performance prototype can read identity card (E-KTP) with a maximum distance is 4 cm, and performance quality of service for an application show that throughput and delay with a perfect index according to standardization telecommunications and internet protocol harmonization over network (TIPHON) depending on what features are being evaluated.

Visually challenged people are facing lots of problems in their day-to-day life. The main aim of the research is to design an intelligent walking stick to guide visually impaired people thereby improving their mobility. In this examination the walking stick is planned with all features of precision and productivity is likewise made high, relatively. The device comprises an array of both ultrasonic sensor and Passive Infrared sensor to detect the static as well as the dynamic obstacle in 180 range of 3m distance in three different directions (left, right, and front). Water sensor is also being attached to the system to detect the presence of both static and dynamic water. When an obstacle is sensed by the sensor, the emergency alert system warns the visually impaired person by a speech warning message. GPS-GSM system is used to track the position of the visually impaired person. On the off chance that the stick is lost, it can be discovered utilizing the remote that actuates the signal to find out the location of the stick. The remote consists of an Rf transmitter and the stick consist of an Rf receiver. Wireless transmission is being carried out between remote and walking stick.

In this paper, an IoT based fire-fighting and affected area monitoring robot is proposed. This fire-fighting robot can be used as a supplementary to the firefighters in critical situations. To function this robot, a flame sensor, a gas sensor, an IR sensor, and a temperature humidity sensor has been used. The flame sensor is used to detect the fireplace at the same time as the gas sensor informs about the presence of flammable gases, the Passive Infrared Sensor confirms the presence of a human, and the temperature humidity sensor sends statistics about the temperature and humidity of the locality. The robot can run in both manual control system and autonomic control system. This paper discusses the detail and top working condition of a fire-fighting robot and recapitulates an IoT based communication system to monitor the fire affected area using Wi-Fi and also discusses the elaborate functions of each module and the implementation of the system. All the data are sent to the cloud server for further investigation. The proposed firefighting robot has been used for many experiments and proper evaluation has been done based on its performance. It has an excellent performance to extinguish the fire in an emergency situation.

Agriculture is the primary source of food supply for all countries in the world. The issue of agriculture has always been hampering the growth and development of the country. To further improve this condition, we need to deploy smart and precision agriculture by revamping the old methods of agriculture. Hence, the paper aims at making agriculture smart and precise by using Internet of things (IoT) technologies. This paper proposes a system design for an IoT-based agriculture monitoring system. It also suggests a cloud-based mobile application for easy monitoring and analyzing the farms at the farmer’s point of view. The proposed work deals with real-time observation by efficiently using the most reliable sensors. The main features of this works are the integration of various agriculture-related sensors such as moisture, temperature, and humidity with the Raspberry Pi and a mobile application. It also stores the recorded data on a ThinkSpeak cloud for future prediction and improving the quality of the crop. This work integrates a passive infrared sensor (PIR) to monitor the intrusion of animals or human interference. The live monitoring of the sensor’s values using cloud computing and a mobile-based application was also analyzed. The proposed system prototype was successfully implemented, executed, and tested. It is a low cost, easy to use for the farmers to efficiently monitor their farms and to improve the crop quality by reducing the wastage of energy and resources.