Baby Weight and Length Measurement System with Data Storage Using MySQL Database

The health sector is crucial today. Various medical devices were created to facilitate the work of health workers. One of them is in the routine measurement of nutritional status by measuring the weight and length of children under five at the Posyandu. Several tools used to measure nutritional status are still working separately, and health workers are still calculating nutritional status manually. The purpose of this study is to design a tool that can measure the nutritional status of toddlers by using measurements of weight and length of toddlers simultaneously based on Anthropometric index calculations and comparing with fuzzy calculation analysis to support the results. The results of nutritional status, in this study, used 2 calculations, namely the calculation of the anthropometric index and the analysis of fuzzy measures. Based on the anthropometric index, the assessment of nutritional status used the variables of weight, body length, age, and gender (M/F). The calculation of these variables is processed in the Arduino Mega 2560 microcontroller and then displayed on the LCD and the Delphi program. A loadcell sensor is used to measure body weight, and a multiturn potentiometer is used to measure body length. Fuzzy calculation analysis used Mamdani, body length/age, and weight/body length methods as input from the anthropometric index and body weight/age to support calculations. The results of this study obtained a measuring tool for the nutritional status of toddlers that can bring up nutritional status based on anthropometric indexes and analysis of Fuzzy Mamdani calculations. This nutritional status measuring device can be used to make it easier for health workers to monitor the development of toddlers

Early childhood talent identification sport programs are needed to decide the potential of children in certain sports. It is expecting that children will be able to maximize sports achievements according to their talents. To find out a human's talent in sports, several parameters can be used for a measurement. One of them is the anthropocentric test, which is a series of processes for measuring the dimensions of the human body with in the form of body height and weight. Based on the results of measurement analysis, children will be able to identify their sport talents. Therefore, a website-based height and weight measurement system was developed that is able to provide information on measurement results and talents identification. The development of this tool uses the ESP 32 Dev Kit Module as a micro controller, an ultrasonic sensor for height sensors and a load cell for a weight sensor. The data are sent to the database contained on the website for further processing and analysis based on the measurement results of talent in the sports field. The measurement website page can display measurement results and identify type of talent that belongs to the child. Based on the results of measurement and data analysis, the accuracy of the tools for measuring body height were 98.7% and body weight were 99.5%, while the precision of the tools were 98.6% for body height and body weight were 97.4%.

The development of the shipping industry in Indonesia has continued to increase over the past 10 years due to the sabotage principle. This development can be seen from the increasing number of national vessels. The number of national vessels becomes wider from 6,041 units in 2005 to 24,046 units in 2016. The number of vessels makes monitoring the operational performance of the vessel difficult because each ship has different travel routes. This research made tools that can monitor the performance of ships especially the use of MFO aboard the ship. The sensors used to measure the volume of MFO in the tank on board are HC-SR04 ultrasonic sensors and potentiometer pendulum sensors. The data from the sensor was processed by Arduino Uno microcontroller. This study would compare the performance of ultrasonic sensors and potentiometer pendulum sensors mounted on MFO oil premises. The study was conducted by measuring the position of the ship horizontally, right and left tilted with a slope level of 30 degrees and 45 degrees. The result of this research is that the potentiometer pendulum sensor was better when MFO surface condition was flat with the average of error sensor 1.60%, while HC-SR04 ultrasonic sensor has an average of error for 2.87%. However, on the skewed surface conditions of MFO, the usage of HC - SR04 ultrasonic sensors was better with average of error for 3.21%, while the potentiometer pendulum sensor had an average of error for 8.66%.

Golden age is the best period for a child's growth, monitoring of growth and development must be carried out regularly. Growth and development disorders in children are stunting and malnutrition. This incident also prompted the government through the Ministry of Health to create a special program towards a golden Indonesia 2045 to monitor stunting and disease, especially in children. The prevalence of stunting decreased to 21.6% from 24.4% in 2022. Early detection of stunting and malnutrition, where the research object is children aged 0-5 years. This prototype was built using a load cell sensor, a study of the use of optical sensors and ultrasonic sensors to measure body height, and a MAX sensor to detect children's anemia. The integration of this tool combines IoT and AI. The results obtained to validate the use of load cells have a reading error of 0.01% with an accuracy of 99%. Comparison using optical sensors and ultrasonic sensors. Optical sensors have result average error of 0.01, accuracy 98.99%, ultrasonic sensors error was 0. 15 with 85% accuracy. To measure malnutrition, the anemia parameters were processed using the Dense Neural Network (DNN) model with 256 neurons showing an accuracy of 98.03%.

The growth and development of a person's body can be affected by various factors such as hereditary factors, the environment and the type of food consumed. One method that can be used to find out the condition of a person's body growth and development is to compare weight and height. This analysis is known as Body Mass Index (BMI). To carry out this analysis, weight and height measurements must first be taken. With the tools developed in this study, the analysis process can be carried out easily. This developed tool is able to measure height and weight at the same time. Height measurement is carried out by placing the weight sensor (Load Cell) under the user's footing like a tire scale in general. As for height measurement, on the top of the patient's head, a laser-based distance sensor VL53L1X placed. In contrast to height measurement which uses a voice-based distance sensor with direct measurements on the surface of the patient's head whose measurement results are less accurate. This is due to the uneven surface of the head, different hair shapes, and diffused sound waves. With the Scale Bar method/plate that can move up/down automatically as the sensor measurement plane. Thus, the measurement results are better because the reflection plane is in the form of a flat plate so that the light reflection results from the laser sensor become more focused. From the results of measurements, weight and height in several patients, the results of measurements were obtained with an average difference of 0.3 cm for height and 0.09 kg for body weight measurement.

Today, having a healthy body is everyone’s dream, from the age of the children, teenagers to adulthood. Personal health must be maintained to run all the activities that are being done. One of the factors that affect the health condition is the nutritional status of a person, which sometimes decreases or increases too high. Body mass index is the most commonly used indicator to know the nutritional status of a person. This system uses two sensors, the load cell sensor as a weight gauge and ultrasonic sensors as a measure of height. Processing data using Arduino Uno and the results of data processing from microcontroller issued to the LCD and stored on the PC database as data Body Mass Index which further processed and displayed the person’s nutritional status directly. After the test, the system successfully measured the weight and height that will be processed into the value of Body Mass Index. The test of this system is performed by randomly selected respondents, both children and adults. Value of the measurement system is also compared with the value using standard tools for measuring weight and height, and the results have near 100% accuracy.

Digitally measuring height, weight, and human body temperature based on Arduino nano, ultrasonic (proximity sensor), Load Cell (weight sensor), and Mlx90614 (temperature sensor) is a tool that serves to measure and get the value of a sensor quantity on a person's body based on certain units. The working principle of this tool is based on ultrasonic reflected light, the pressure of an object, and infrared radiation. The system design includes the working principle of ultrasonic sensors, load cells, Mlx90614, arduino nano, 16x2 lcd + i2c lcd, lm2596 step down module. This device circuit system is a circuit capable of changing a length, weight, and temperature so that it can be processed and displayed in the form of an electrical system. The process of change is by converting distance, weight, and temperature into analog voltages, after going through the signal conditioning process by amplifying, the analog voltage is converted into digital data. The digital data obtained is then processed by Arduino and displayed. The results of the performance of this tool have an average measurement error of 1.16% for measuring height, -0.69% for measuring weight, and 4.35% for measuring body temperature.
 Keywords: Arduino Nano, Ultrasonic Sensor, Load Cell Sensor, Mlx90614 Sensor.

Human activity recognition is an important problem in health care, ambient-assisted living, surveillance-based security, etc. and has crucial applications in smart environment. A non-invasive, automated system for monitoring human activity using array of heterogeneous ultrasonic sensors has been proposed in this work. Ultrasonic sensors are widely used for distance measurement in many applications. In the proposed system experiments have been conducted using ten volunteers in a controlled laboratory environment. The data collection unit has two kinds of setups of ultrasonic sensors: the former with five HC-SR04 sensors, and the latter with four HC-SR04 ultrasonic sensors and an LV-MaxSonar-EZ0 sensor. The proposed method is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. Based on the collected data, we have performed classification analysis using multiple machine learning algorithms. The experimental results show 81% to 90% correct detection of different activities of the volunteers.

The HC-Sr04 ultrasonic sensor (proximity sensor) is a widely used ultrasonic sensor, in addition to its affordable price also because of its easy use and easy installation. Ultrasonic sensors are electronic devices whose ability can convert from electrical energy into mechanical energy in the form of ultrasonic sound waves. HC-SR04 sensor is one of the ultrasonic sensors that is often used to monitor the distance of objects (objects) with sensors. This sensor consists of a series of ultrasonic transmitters called transmitters and ultrasonic receivers called receivers. The distance that can be handled ranges from 2 cm to 400 cm, with a precision level of 0.3 cm. The detection angle that can be handled is no more than 15°. The required current is not more than 2mA and the required voltage is +5V. The number of pins is 4. In this study, the authors used the fuzzy logic method to help classify the level of distance received by the sensor. Fuzzy logic is used because this method is able to determine the classification results of the received values, according to what is needed by the distance measuring device with this ultrasonic sensor.

Visual impairment significantly hinders daily activities, necessitating innovative solutions. This study develops and tests a smart walking stick using NodeMCU ESP-8266, HCSR-04 ultrasonic sensor, DFPlayer, and GPS, monitored via the Blynk app. Employing a research and development approach, the device demonstrated 100% accuracy in obstacle detection and location tracking, providing real-time feedback through audio notifications and smartphone monitoring. This smart walking stick enhances mobility and safety for visually impaired users, representing a significant advancement in assistive technology. Highlights: Accurate Obstacle Detection: 100% accuracy with HCSR-04 ultrasonic sensor. Real-time Monitoring: GPS tracking via Blynk app. Enhanced Mobility: Increases safety and independence for visually impaired users. Keywords: Visual impairment, Smart walking stick, Ultrasonic sensor, GPS tracking, IoT

<h3>Aims</h3> A child9s nutritional status often deteriorates over the course of a hospital admission. No universally accepted paediatric nutritional screening tool exists. This study aimed to evaluate validity and ease of use two new instruments, STAMP and STRONG, for the assessment of nutritional risk of paediatric inpatients. <h3>Methods</h3> In a cross-sectional study two trained investigators applied STAMP and STRONG, respectively, to eligible inpatients. Demographic data, clinical information, dietetic input and measurements of weight and height were recorded. Statistical analysis used weight-for-height (height &lt;120 cm) and body mass index (height &gt;120 cm) as proxies for acute undernutrition and height-for-age for chronic undernutrition. Correlation of assessed risk and two factors predictive of nutritional risk: anthropometric nutritional status and presence of nutritional intervention were used to evaluate validity of the instruments. <h3>Results</h3> 43 children (mean age 6 years and 4 months) were assessed by STAMP and STRONG. Using STAMP the population was classified as 44% high risk, 28% medium risk and 28% low risk. Using STRONG, 25% were high risk, 46% were medium risk and 29% low risk. For validity, STAMP scores correlated to weight-for-height (p&lt;0.01) and height-for-age (p&lt;0.01) but correlation was not significant for body mass index (p&gt;0.05). STRONG correlated to all anthropometric measures (p&lt;0.01). For STAMP and STRONG, 60% and 83%, respectively, of children ranked as high risk received nutritional intervention. Both tools identified all of the acutely malnourished (Z score less than −2 weight-for-height, or body-mass-index) and seven of eight chronically malnourished children (Z score less than −2 height-for-age). STAMP took 10–15 min to apply, and involved measuring children9s weight and height, and STRONG took 5–10 min. <h3>Conclusion</h3> In terms of validity; STAMP correlates less closely to anthropometric assessment of nutritional status and identifies considerably more children who receive no nutritional intervention as high risk than STRONG. This suggests STAMP over-diagnoses nutritional risk. In terms of ease of use STAMP involves measurement of each child9s weight and height. Only 12.5% of inpatients had heights recorded. STAMP takes approximately 10 min longer than STRONG. Both for validity and ease of use STRONG was superior to STAMP.

The accumulation of waste in certain locations, especially in residential areas, due to the continuous accumulation of waste can cause environmental disturbances such as disease and unpleasant odors. This system is designed to find out whether the trash bin is full or not by applying fuzzy logic, if the status of the trash bin can be cleaned, it will be handled immediately so that the waste does not accumulate and disturb people around. This system can find out when the last time the waste was taken and the location of the trash bin as a prototype which can later be applied to areas with a wider range by cleaning service officers in Mamuju City. This system uses the HC-SR04 sensor to detect the distance of the waste to the sensor and uses the Load Cell sensor to detect the weight of the waste. Several tests were carried out, first by measuring the accuracy of each sensor used, the HC-SR04 sensor accuracy was obtained at 96.68% with an error of 3.32%. While the accuracy of the load cell sensor is 90.68% with an error of 9.32%. The second test calculates the sensor response time and Blynk notification response since the sensor detects waste, the average HC-SR04 sensor detection response is around 0.83 seconds. For the response time of incoming notifications when there is movement in the HC-SR04 sensor area has an average of 2.65 seconds. While the Load Cell sensor response time is only around 0.53 seconds. For Blynk notification response time since the Load Cell sensor detects it has an average of 2.51 seconds. The third test calculates the response time of the two sensors (HC-SR04 and Load Cell) and the Blynk notification response since the two sensors detected the waste, the average response time of the two sensors finished detecting only around 0.91 seconds. For the response time of the trash bin status condition, if there is movement in the area of the two sensors, the condition of the trash bin will change and display the status of Normal, Needs Cleaning and Highly Needs Cleaning with an average response time of 1.18 seconds. The system successfully sends notifications according to the fuzzy rules and expected to speed up the waste handling process

The rapid development of technology has created various conveniences in all aspects of human life. One of the fields of technology that is rapidly developing is the world of electronics, which demands digitization to facilitate human activities. For example in the field of trade, humans want a process that is practical and easy. Weight measurement is one of the problems that hinders the trading process, because it is still done manually and is less efficient. The purpose of making a design of automatic digital scales based on fruit images using the yolo method (you only look once) is to determine the type and weight of fruit using a Load Cell sensor simultaneously and accurately. The working principle of this tool is the first, the detection of fruit types is processed by a USB Webcam by taking video from the Webcam and then processing it using the Yolo method to identify the type of fruit. The second stage is input from the Load Cell sensor sending object data read to the Raspberry Pi as the main controller. to determine the weight of the fruit, then the last stage is displayed on the LCD and the results can be printed out as proof of purchase receipt. From the LCD directly displays output in the form of price, weight and type of object being weighed. The Yolo method can detect objects and colors and has high detection speed and accuracy.&#x0D;

The development of ultrasonic sensor technology used for suitable equipment is widely carried out, such as distance sticks for the blind, floor cleaning robots, automatic garbage boxes, height measuring instruments, water volume detectors, automatic dispensers, automatic hand sanitisers, etc. During the COVID-19 pandemic, a product that can be used to limit the distance between humans is needed, so this research is focused on products for measuring distances with ultrasonic sensors. The research design method used is research and development, using the Borg and Gall approach, with step information collecting, planning, development, testing, final product and implementation. The results of this research product are designed using Arduino nano v3 hardware, battery, buzzer, and ultrasonic sensor HC-SR04. Product design was registered with Industrial Design Intellectual Property Rights with registration number A00202102408. The results of measuring the quality of the ultrasonic sensor for measuring distances on flat objects show an average error percentage of 2.94% from 10 cm to 300 cm with a distance range of 10 cm. Tests on obstacle objects (humans) at an angle of zero degrees and 15 degrees showed the best farthest measuring distance at a distance of 150 cm. So 150 cm is the best distance to apply to the hat COVID-19 reminder. The development results show that the product is ready to continue in production and sales.

This paper presents the design, development, and deployment of an IoT- based height and weight measuring machine, providing real-time and automated measurement of height and weight through integrated sensors. The system employs ultrasonic and load cell sensors connected to an Arduino-based microcontroller, which processes and transmits the data via wireless communication to a cloud-based system. The cloud platform enables remote access, real-time monitoring, and data storage. The proposed solution is cost-effective, scalable, and capable of enhancing personalized health management while supporting healthcare professionals by providing accurate and accessible health data. This paper outlines the system architecture, methodology, results, and future prospects of the system.