Room Monitoring Uses ESP-12E Based DHT22 and BH1750 Sensors

Comfortable room is one of the services that must be provided by STMIK STIKOM Indonesia campus to students. This research designed a room monitoring tool based on ESP-12E in STMIK STIKOM Indonesia. The room monitor is designed using a DHT22 sensor to measure temperature and humidity and the BH1750 sensor to measure light intensity. The tool also includes features a 16x2 I2C LCD to display measurement results. Testing is done by testing the layout circuit on the PCB and observing the measurement of temperature, humidity, and light intensity on the LCD. The test results of all layout circuits are functioning properly, and the measurement results can appear on the 16x2 I2C LCD

Integrated Room Monitoring and Air Conditioning Efficiency Optimization Using ESP-12E Based Sensors and PID Control Automation: A Comprehensive Approach

This study addresses the critical need for efficient room monitoring and air conditioning systems, particularly in educational settings like the STMIK STIKOM Indonesia campus. The paper introduces a novel approach that combines ESP-12E based sensors with Proportional-Integral-Derivative (PID) control automation to optimize air conditioning efficiency. Utilizing an ESP-12E microcontroller, the study designed and implemented a room monitoring tool equipped with DHT22 and BH1750 sensors for accurate measurement of temperature, humidity, and light intensity. We also explores the integration of a PID control system into an existing air conditioning (AC) unit. The PID controller was fine-tuned to maintain a stable indoor temperature of 25oCelsius, even when subjected to external heat loads, such as ten LED lamps. The effectiveness of this system was quantified through real-time monitoring of temperature, humidity, and energy consumption, both pre- and post-implementation. Results indicated a rapid and stable response from the PID controller, achieving an amplitude of 1 within 0.08 seconds, thereby confirming its successful tuning and adaptability. We found that this study has broader implications for enhancing energy efficiency and creating conducive learning environments. However, it is worth noting that the research was conducted under specific conditions, and further studies could explore its applicability in different settings

A Novel Deep Learning, Camera, and Sensorbased System for Enforcing Hand Hygiene Compliance in Healthcare Facilities

Hospital-acquired infections are a major cause of death worldwide, and poor hand hygiene compliance is a primary reason for their spread. This paper proposes an artificial intelligence, microcontroller, and sensor-based system that monitors and improves staff hand hygiene compliance at various critical points in a hospital. The system uses a Convolutional Neural Network (CNN) to detect and track if staff have followed the WHO hand rub/hand wash guidelines at alcohol dispensers, hospital sinks, and patient beds. The system also uses RFID tags, vibration motors, LEDs, and a central server to identify staff, alert them of their cleaning requirements, monitor their cleaning activity, and report compliance data. We obtain an accuracy of 90.6% in classifying all steps of the WHO-stipulated hand wash/hand rub guidelines and a testing accuracy of 89.8% on Ivanovs et al.’s dataset. The system ensures that hospital staff stay compliant to all WHO hand hygiene guidelines, saving countless lives

Design criteria for Indoor Positioning Systems in hospitals using technological, organizational and individual perspectives

This dissertation considers three different studies that handle Indoor Positioning Systems (IPS) in hospitals. Study 1 uses the Reasoned Action Approach by questioning hospital visitors and employees about their intention to use IPS in hospitals. Study 2 reviews IPS in hospitals. Study 3 is based on the results of the first two studies. It handles expert interviews that were conducted with different hospitals and IPS developers to evaluate the determined propositions. Then, the insights were used to conduct and evaluate experiments by testing an ultrasound-based IPS for hospitals

IoT based Hand Hygiene Compliance Monitoring

International Symposium on Networks, Computers and Communications (ISNCC) -- JUN 18-20, 2019 -- Istanbul, TURKEYURAL, Cemil Aybars/0000-0002-8531-3430; Challenger, Moharram/0000-0002-5436-6070WOS: 000520478600065Compliance monitoring and surveillance is an important task in hospitals and health centers as the health-care associated infections can have serious consequences such as increasing mortality and accelerating morbidity. Monitoring and surveillance by human observers have its own difficulties such as loss of observer concentration, human error build-up, and professional deformation. in this work, we have implemented and compared the performance of two Internet of Things based approaches for monitoring the hand hygiene of medical staff during patient visiting. We used ESP modules as base stations and smart-phones as mobile nodes and estimated the distances using Bluetooth RSSI values to locate the medical staff in a patient's room. in the proximity-based solution, we compared the RSSI from a mobile node measured on different ESP nodes/modules and utilized the assumption stating that the mobile node is closest to an ESP node which yields the highest RSSI value. in the trilateration based approach, we used the RSSI values to estimate the distance of mobile nodes to each ESP node and we used a trilateration algorithm to locate the mobile node in the room. Our experiments showed that the proximity-based solution recorded 20% incorrect location visiting while the percentage for the trilateration based solution was 8%. This indicates that the trilateration based approach is more reliable than the proximity-based solution in this application domain.IEEE, IEEE Commun Soc, TEST, CIS ARGE, AIR[16-UBE-002]; [17-UBE-002]This study is realized in the scope of the Scientific Research Projects No 16-UBE-002 and No 17-UBE-002 at EGE University, Izmir-Turkey