Barometric Pressure Sensor Research Articles

DESIGN AND FABRICATION OF SMART ENVIRO GUARDIAN

This project tackles the challenge of maintaining a comfortable and healthy indoor environment in the face of unpredictable weather conditions. The primary objective is to develop an automated weather monitoring system integrated with a humidifier, leveraging Internet of Things (IoT) technology and a suite of sensors including the DHT11 for temperature and humidity, a rain sensor, an LDR sensor, and a BMP180 barometric pressure sensor. The system aims to monitor real-time weather data and adjust indoor conditions accordingly, enhancing comfort and reducing health risks such as respiratory issues, allergies, and mold growth. Additionally, it provides remote monitoring and real-time alerts through IoT connectivity, improving user convenience and energy efficiency. The methodology includes detailed requirement analysis, hardware and software development, testing, and deployment. This project promises to significantly improve indoor environmental quality, especially in regions with erratic weather patterns, by providing a robust, adaptable solution. Keywords: Automated Weather Monitoring, Climate Control, IoT Technology, Humidifier, DHT11 Sensor, Rain Sensor, LDR Sensor, Energy Efficiency

Integrated Barometric Pressure Sensors on Legged Robots for Enhanced Tactile Exploration of Edges

Integrated Barometric Pressure Sensors on Legged Robots for Enhanced Tactile Exploration of Edges

An Enhanced Indoor Three-Dimensional Localization System with Sensor Fusion Based on Ultra-Wideband Ranging and Dual Barometer Altimetry.

Accurate three-dimensional (3D) localization within indoor environments is crucial for enhancing item-based application services, yet current systems often struggle with localization accuracy and height estimation. This study introduces an advanced 3D localization system that integrates updated ultra-wideband (UWB) sensors and dual barometric pressure (BMP) sensors. Utilizing three fixed UWB anchors, the system employs geometric modeling and Kalman filtering for precise tag 3D spatial localization. Building on our previous research on indoor height measurement with dual BMP sensors, the proposed system demonstrates significant improvements in data processing speed and stability. Our enhancements include a new geometric localization model and an optimized Kalman filtering algorithm, which are validated by a high-precision motion capture system. The results show that the localization error is significantly reduced, with height accuracy of approximately ±0.05 m, and the Root Mean Square Error (RMSE) of the 3D localization system reaches 0.0740 m. The system offers expanded locatable space and faster data output rates, delivering reliable performance that supports advanced applications requiring detailed 3D indoor localization.

Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison.

Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are furtherdiscussed.

A customized drone for ocean and atmospheric measurements and its performances

Drones- Unmanned Aerial vehicles (UAVs)- have become more significant across the world for a wide range of commercial and terrestrial defense applications in recent years. The National Institute of Ocean Technology (NIOT)- Ministry of Earth Sciences (MOES)- is exploring UAVs with a specific focus on maritime applications. NIOT has customized a heavy lift category drone, operable in marine environments and capable of withstanding coastal wind conditions of up to 40 kmph. It can lift and carry an instrumentation payload of 10 kg, conduct ocean data collection, and even take seawater samplings. The 10 kg payload of the UAV might hold a Conductivity Temperature Depth (CTD) sensor, a programmable sea water sampler, and a multi-parameter sensor (MPS) for ocean data collections, and a Light Detection and Ranging (LiDAR) device integrated with a high frame rate camera system for coastal mapping and digital elevation model (DEM) developmental applications. The customized hexacopter UAV is capable of withstanding winds of up to 10 m/s and features a waterproof IPX7 thruster with a maximum thrust of 153 N per axis. The UAV system is also interfaced with a Global Positioning System (GPS), a barometric pressure sensor, a compass, a highly accurate gyroscope, a 15 MP surveillance camera, and an accelerometer sensor connected to a reliable cube orange flight controller module with a redundant 32-bit controller through a serial peripheral interface (SPI). The drone structure and frame is composed of carbon fiber composites to provide an excellent weight-to-strength ratio. This paper presents the outcomes of an initial field test, carried out to ensure the drone’s suitability for various marine applications with intended payloads ranging from 5 - 10 kg weight, including coastal demonstrations and ocean data collections performed at Nellore (Andra Pradesh) and Chennai (Tamil Nadu) coastal waters. Highlights A drone is customised to with stand elevated wind conditions of coastal regions Design is fully flexible meeting the ease of transport from one location to other Highly stable mechanical design to sustain winds of up to 45 KMPH Redundant flight control mechanism is evolved to assure fail safe operations of drone Drone built in with redundant GPS module to avoid scenario like NO Geo-reference at any time Design is type certified and UIN registered with DGCA - India

System design and implementation of an IoT electronic pulse sphygmomanometer

A sphygmomanometer is a standard testing tool for monitoring blood pressure and plays a vital role in modern medical diagnosis. Traditional smart electronic sphygmomanometers are complex and costly; this paper designs a low-cost electronic pulse sphygmomanometer through microcontroller control. This design collects the gas pressure information through the barometric pressure sensor and inputs it into the analog-to-digital converter. It converts the collected analog signal into a digital signal and then completes the determination of blood pressure and pulse through the recognition algorithm. Finally, this design is found to have the advantages oa f wide range of applicationslightweightht, low price, etc., and has important application value.

Microstructured Gel Polymer Electrolyte and an Interdigital Electrode-Based Iontronic Barometric Pressure Sensor with High Resolution over a Broad Range.

We present a novel iontronic barometric pressure sensor based on a gel polymer electrolyte and interdigital electrodes with a much simpler structure than that of existing devices. By introducing high-density microstructures on the gel polymer electrolyte and one side electrode arrangement configuration, the developed sensor offers high performances with an ultrahigh resolution of 10 Pa, an ultrawide barometric pressure-response range from -92 to 7 kPa, a fast response time of ∼15 ms, and excellent long-term stability. The single pressure sensor is able to detect positive and negative barometric pressures without needing any additional means and can operate as a barometric altimeter with a resolution of about one-floor height. The performances of the sensors significantly surpass those of existing barometric pressure sensors. This work provides a new strategy for making high-performance barometric pressure sensors that are highly sought for commercial applications such as altitude detection, negative pressure ambulance, and consumer electronics.

Unravelling the gait and balance: A novel approach for detecting depression in young healthy individuals

Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements

Design and Development of a 3D-Printed CubeSat by Go Science

This project outlines the design, development, and construction of a 3D-printed CubeSat, a miniature satellite, equipped with an ESP32 microcontroller, an MPU9250 inertial measurement unit, and a BMP280 barometric pressure sensor module. The CubeSat adheres to standard CubeSat dimensions and incorporates a range of systems, including power management, communication, and data collection, to enable its functionality in space. The CubeSat's structure was meticulously designed using CAD software, and 3D printing technology was employed to fabricate its lightweight, space-grade casing. The ESP32 microcontroller serves as the central processing unit, interfacing with the MPU9250 and BMP280 sensors via I2C communication. Custom firmware was developed to gather sensor data and manage power resources efficiently. Solar Panels are also used for redundant Power Support. The CubeSat features a wireless transceiver for communication with ground stations, facilitating telemetry data transmission and command reception. This CubeSat can collect sensor’s data and transmit it to Ground Station for analysis, contributing valuable insights into its surrounding environment. Maintenance and potential upgrades will be considered to extend its operational lifespan. This project exemplifies the intricate process of designing and developing a CubeSat, highlighting the integration of advanced sensors and microcontrollers in a 3D-printed structure. The CubeSat's deployment underscores its potential for scientific research and data collection in the realm of space exploration. Developed by Go Science Technical Team. IndexTerms – Go Science, CubeSat, ESP32, MPU9250, BMP280, Environmental Monitoring, Space Technology, Sensor Integration, Data Collection, Climate Research, Microsatellites, Miniature Satellites, Space Sensors, Atmospheric Data, Earth Observation, Remote Sensing, Environmental Science, Climate Monitoring, Sensor Fusion, Space Exploration.

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

This paper presents an analysis of a quadcopter’s stability and its sensors’ data reading using an IMU (inertial measurement unit). Firstly, the angular velocity and acceleration data are read by an Stm® development board embedded with an Stm32f401ccu6 microcontroller. The altitude of the measurement instrument platform was determined using an MS5611 barometric pressure sensor combined with a temperature sensor. The quadcopter’s control was achieved by connecting a brushless DC motor to the Stm® board, which received four PWM (pulse-width modulation) signals via the output port. An electronic governor was utilized to control the brushless DC motor, while a pre-existing remote control was designated as the transmitter. The quadcopter receiver received a 2.4 GHz signal from the transmitter using the BLE (Bluetooth low-energy) protocol, which was used to ensure the simultaneous operation of the four brushless DC motors. Finally, a PID (proportional–integral–derivative) controller was employed for parameter adjustment. The collected PID parameter program was developed in the Simulink software as a simulation platform, allowing for the execution of the Simulink model on the Stm® MCU. The Stm® module facilitates monitoring of the performance of the UAV (unmanned aerial vehicle) and enables immediate parameter adjustments to ensure flight stability. This research aims to reduce calculation errors in sensor and controller usage and improve the efficacy of the remote machine module for future industrial applications.

Examining gender differences in gait parameters between non-smoker and smoker participants.

Smoking is one of the predictors of decreased cardiopulmonary endurance. Gait disturbance may be due to many reasons, including cardiovascular endurance. This study aimed to determine differences in gait parameters between non-smoker and smoker participants. A cross-sectional design was employed, involving thirty non-smokers and thirty-seven smokers as participants. Detailed interviews were conducted to gather information on smoking habits, status, and history. Gait parameters were measured using a high-quality 3D accelerometer, 3D gyroscope, and barometric pressure sensors (Physilog4 from GaitUp). Anthropometric characteristics were described, and mean values with standard deviations (SD) were calculated. An independent two-tailed t-test was conducted to compare gait parameters between non-smokers and smokers, with statistical significance set at p<0.05. The analysis revealed significant differences in various gait parameters between non-smokers and smokers. Specifically, significant differences were found in cadence (t=9.95, p=0.001), stride length (t=6.85, p=0.001), stride velocity (t=-6.58, p=0.001), stance (t=2.02, p=0.001), swing (t=3.46, p=0.001), foot flat (t=-8.94, p=0.001), pushing (t=3.53, p=0.001), and double support (t=-13.35, p=0.001). However, no significant difference was found between non-smokers and smokers in the loading phase (t=-1.57, p= 0.121). There were significant differences in general and temporal gait parameters between smokers and non-smokers. Gait parameters provide valuable insights for evaluating functional performance and providing objective and quantitative data to assess gait disorders. Future studies should include longitudinal studies with large sample sizes to explore the effects of potential confounders on gait parameters.

Vacuum booster control system design and fault self-check strategies

In this paper, the software and hardware design of the vehicle vacuum pump control system was carried out based on single-chip microcomputer technology. Combined with the vacuum pump structure and control principle, the hardware design of the power supply, barometric pressure sensor, vacuum pressure sensor, current detection, and motor drive is carried out with the HS08 series single-chip microcomputer as the core. The software strategy design for barometric pressure signal acquisition, vacuum pressure signal acquisition, and vacuum pump driving has been completed, and the dual pressure vacuum pump control system for electric vehicles has been developed. The system can diagnose the common faults of the vacuum power system. Finally, the functional test of the control system is carried out. The results show that the system can work normally and that the design meets the expected requirements.

Real-time underground route identification and route progress using simple on-board sensing and processing

The global emphasis on conserving energy resources has led to the adoption of hybrid power systems in vehicles. Optimally applied, hybrid systems can save up to 40% on fuel costs. To optimally manage a hybrid vehicle's energy flow it is necessary to know, in real time, all the energy requirements to complete a given route. The energy consumption depends mainly on the vehicle mass, speed profile, and route topography. Of these, the topographic profile is the one factor that is only route-dependent and not influenced by the vehicle or driving styles. The heading vs. distance profile is also an example of a route characteristic not influenced by the vehicle or driving style. In this study the topographic and heading profiles are used to identify routes, and are easily measured by means of digital barometric pressure and compass sensors. Correlations between the current route and previously travelled/stored routes are performed based on their topographic and heading profiles in a point-by-point manner. Above-ground tests were first performed using a road vehicle and six routes to evaluate the system. The system consistently and correctly identified a 20 km route within the first 4 km. It also proved to function correctly in underground tests through the implementation of an instrumented hand-held surveyor's wheel. This system finds direct practical application in optimizing the energy management of an underground hybrid locomotive used by the mining industry in South Africa, but can also be of benefit in applications where route identification is required and using GPS is not feasible.

Identification of Soil Features Suitable for Barley, Maize and Sugarcane Cultivation using Precision Agriculture

Agriculture plays a crucial role in a nation's economy by significantly contributing to its Gross Domestic Product (GDP). Improved methods of farming not only benefit farmers by lowering their workload but also boost crop yields. Identifying the optimal soil for each crop is essential for producing harvests of the highest quality and quantity. Precision farming in agriculture uses Internet of Things (IoT) technology to ensure the most efficient use of available resources to increase crop yields and reduce operational costs. It reduces instances of crop failure and aids in averting catastrophic losses. Variations in soil temperature and soil moisture have the most significant influence on crop yield. The Soil Moisture Sensors calculate the soil's total volumetric water content. Using the BMP280 absolute barometric pressure sensor, it is possible to get accurate temperature and air pressure measurements. In the paper, the primary purpose of precision agriculture usage is to determine the optimal soil characteristics for the production of Barely, Maize and Sugarcane crops.

BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability.

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75-150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.

Discrete room pressure drops predict door openings and contamination levels in the operating room setting

BackgroundContamination of operating room air is associated with increased risk of surgical site infection. Door opening events occur often during surgery. The effect of door opening on room pressure and contamination is investigated, and detection of door opening events via a single internal barometric pressure sensor is demonstrated. MethodsA single barometric pressure sensor stationed in a corner away from the door was used to measure room pressure in a simulated OR with air flow conditions of 20 ACH and a positive pressure of approximately 7.5 Pa. A laser-based particle counter was used to measure particle levels during a series of 20 periodic door opening events. ResultsThe average (standard deviation) detected room pressure in the OR under positive pressure decreased by approximately 6.5 (1.2) Pa (p>3 × 10−17). When the door is closed the pressure returns. Initial airborne particle levels in the room were 36 (6) /ft3. 20 periodic door openings performed over 40 min. resulted in particle levels exceeding 200/ft3. ConclusionsOR barometric pressure decreases and recovers when the door to an OR under positive pressure is opened and closed and provides a detectable signal for passively monitoring and tracking door openings. The number of particles in the unoccupied OR increases as increasing numbers of door opening events occur.

Automated Barometric Chamber for Entomology Experiments: Arthropods’ Behavior and Insect-Plant Interactions

Insect behaviors, such as flying, oviposition, parasitism, mating/calling, response to semiochemicals, and others, might be influenced by barometric pressure fluctuations. Abiotic factors controlled in the laboratory facilitate the observation of particularities related to development, behavior, and/or habits of arthropods and plants and their interactions. This study aimed to design an automated barometric chamber for research on arthropod behaviors and insect–plant interactions in the laboratory. The barometric chamber is a transparent box equipped with a single-board computer. An air pump and two proportional solenoid valves were used as actuators to control the air flow, while barometric pressure, air humidity, and temperature sensors were used to monitor the conditions within the chamber. A graphical user interface to operate the barometric chamber was developed to run in a web browser. The barometric chamber was designed to allow the barometric pressure to be changed by up to 15 hPa with respect to the local barometric pressure. In addition, the control system makes it possible to set the rise/fall time (ramp) corresponding to the duration in which a change of pressure will be conditioned. Short- and long-term evaluations demonstrated that the control system can assure pressure stability of ±0.1 hPa with respect to the setpoint value. For demonstration purposes, two experiments were carried out to evaluate the influence of barometric pressure on the feeding activity of Euschistus heros and Diabrotica speciosa. For E. heros, the number of stylet sheath was significantly increased under high pressure conditions compared to the low pressure. However, for D. speciosa, there was no statistical difference in leaf consumption at the evaluated testing conditions.

IoT Based Smart Agricultural System for Efficient Crop Growth

Population growth has made agriculture the most important growth sector in the world. The challenge in the agricultural industry right now is to improve the efficiency and quality of agriculture without regular physical monitoring to meet the rapidly increasing food demand. Along with population growth, climatic condition is also a major challenge in agriculture. The purpose of this study is to propose an internet-based smart agricultural model of things that uses clustering to deal with adverse conditions. This model uses different types of sensors for different purposes, such as soil moisture, barometric pressure, rainfall detection, and humidity sensors. The data is collected in the cloud and calculated automatically. Intelligent agriculture is automatically taken over by plant management, useful data collection and analysis. The purpose of this white paper is to implement the Internet of Things (IoT) to monitor humidity, soil conditions, temperature and field water supply, water levels, and climatic conditions. The IoT-based smart farming system envisioned in this report is integrated with a variety of sensors and a Wi-Fi module that produces live data feeds that can be accessed online. Keywords: Smart farming, IOT, Clustering, Sensors, Cloud.

Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device's Data.

With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.

Development of position calculation method using elevation data by barometric pressure sensor

The global navigation satellite system (GNSS) is used to help people get from A to B by determining the position of us and the vehicles we use for travel. However, there are issues surrounding the GNSS, including its accuracy. This is due to a variety of factors that can cause radio waves coming in from satellites to experience poor reception. In addition, cybercrime is a concern for the GNSS as with cybercriminals becoming more sophisticated, they could one day interfere with the satellite system, which would cause havoc. Dr Takayoshi Yokota, Graduate School of Engineering, Tottori University, Japan, is working to overcome these challenges by developing a method for estimating the position of a travelling vehicle based on altitude profile, driving behaviour, road surface roughness and changes in inclination. Yokata and his team are working to enhance the effectiveness of Micro Electro-Mechanics System (MEMS) sensors. This work involves developing a position calculation method using elevation data by barometric pressure sensor as altitude can be indirectly measured using pressure sensors and this is a convenient method as these sensors are embedded in most smartphones. In order to aid localisation, acceleration data and angular velocity data are required along with a pressure reading that can be used to calculate altitude. Yokota anticipates that drivers will benefit from the technology in due course.