Sensor Setup Research Articles

A Survey on Women Safety and Health Monitoring System

Women safety is a very big issue even in this modern world running with advanced technologies. After a history of times, women got a freedom of equality at the workplace, asset rights, family law, and education. Even Feminist Movement, claimed during 20th Century, Women are not safe anywhere and violence against women and girl child’s happening everywhere during the lonely travelling on roads, picnic spots, work and deserted places. Though we have many armies and super forces to protect everybody, the day to day crime rate against women has not been reduced. There are several safety devices available to protect the women during the violence, but need more human intervention such as entering the data, or shaking the device roughly. We propose a solution for a secure and peaceful environment for women with handbag safety hand-held devices with the aim to provide false proof women safety devices by overcoming the disadvantages in existing system. When we are talking more about Women empowerment, Women achievements it is also very important to think about women safety since a huge numbers (848) of women’s are Indian Women Are Harassed, Raped, Killed Every Day. The proposed work aims at IoT based women safety device by hardware controller attached to the handbag, android application and Bluetooth connectivity in Smartphone. By pressing the controller button, the device alerts the first holder, relatives stored in the database and police when a woman is not safe. The main advantage of the work is, the device works without internet connectivity. Additional features such as protecting the valuable things to be stolen by thieves in crowded places or buses by a separate alarming system, heartbeat sensor setup to monitor heart rate, fingerprint scanner for effecting accessing of the devices and also the mobile android application provide the victim’s location to reach the women and safeguard the women from any harassment at the right time. The main thought of this paper is that it will not only protect the women from physical harassment during odd hours but also safe the women health when it is found abnormal, as a single device comparatively.

Complexities and challenges of translating intervention success to real world gait in people with Parkinson's disease.

Unstable gait leading to falls negatively impacts the quality of life in many people with Parkinson's disease (PD). Systematic review evidence provides moderate to strong evidence of efficacy for a wide range of physiotherapy-based interventions to reduce gait impairment. However, outcomes have often focused on gait assessments conducted in controlled laboratory or clinical environments. This perspective investigates the complexities and challenges of conducting real-world gait assessments in people with PD and the factors that may influence the translation from improved lab-assessed gait to improved real-world gait. Through a thorough review of current literature, we present an in-depth analysis of current methodological approaches to real-world gait assessments and the challenges that may influence the translation of an intervention's success from lab-based outcomes to improved walking during daily life. We identified six key factors that may influence the translation of intervention success into real-world environments at different stages of the process. These factors comprise the gait intervention, parameters analyzed, sensor setup, assessment protocols, characteristics of walking bouts, and medication status. We provide recommendations for each factor based on our synthesis of current literature. This perspective emphasizes the importance of measuring intervention success outside of the laboratory environment using real-world gait assessments. Our findings support the need for future studies to bridge the gap between proven efficacy for gait as assessed in controlled laboratory environments and real-world impact for people with PD.

Green Wearable Sensors and Antennas for Bio-Medicine, Green Internet of Things, Energy Harvesting, and Communication Systems.

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The planet is suffering from increasingly wild storms, hurricanes, typhoons, hard droughts, increases in seawater height, floods, seawater acidification, decreases in groundwater reserves, and increases in global temperatures. These climate changes may be irreversible if companies, organizations, governments, and individuals do not act daily and rapidly to save the planet. Unfortunately, the continuous growth in the number of computing devices, cellular devices, smartphones, and other smart devices over the last fifty years has resulted in a rapid increase in climate change. It is severely crucial to design energy-efficient "green" technologies and devices. Toxic waste from computing and cellular devices is rapidly filling up landfills and increasing air and water pollution. This electronic waste contains hazardous and toxic materials that pollute the environment and affect our health. Green computing and electronic engineering are employed to address this climate disaster. The development of green materials, green energy, waste, and recycling are the major objectives in innovation and research in green computing and electronics technologies. Energy-harvesting technologies can be used to produce and store green energy. Wearable active sensors and metamaterial antennas with circular split ring resonators (CSSRs) containing energy-harvesting units are presented in this paper. The measured bandwidth of the matched sensor is around 65% for VSWR, which is better than 3:1. The sensor gain is 14.1 dB at 2.62 GHz. A wideband 0.4 GHz to 6.4 GHz slot antenna with an RF energy-harvesting unit is presented in this paper. The Skyworks Schottky diode, SMS-7630, was used as the rectifier diode in the harvesting unit. If we transmit 20 dBm of RF power from a transmitting antenna that is located 0.2 m from the harvesting slot antenna at 2.4 GHz, the output voltage at the output port of the harvesting unit will be around 1 V. The power conversion efficiency of the metamaterial antenna dipole with metallic strips is around 75%. Wearable sensors with energy-harvesting units provide efficient, low-cost healthcare services that contribute to a green environment and minimize energy consumption. The measurement process and setups of wearable sensors are presented in this paper.

Electrochemical Biosensor Enabling Sensitive Detection of miRNA Biomarkers

Biomarkers are becoming increasingly important for early diagnosis, exploration of responses to medication, and assessment of therapeutic efficacy of various diseases [1]. Increasing evidence shows that miRNAs can be employed as biomarkers for cancer and cardiovascular diseases. In this context, the development of powerful electrochemical diagnostic tools has grown in interest due to their sensitivity, speed, and low cost [1]. Additionally, electrochemical technologies offer a facile means to fabricate well-defined nanostructured surfaces in a controlled environment. The gold (Au)-based surface stands out for its ability to improve charge transfer characteristics, enhancing conductivity. It also serves as a stable substrate for further modification, enabling the detection of target analytes [2]. Here, we utilized an electrochemical platform by using a 3-electrode carbon-based screen-printed electrode (SPE) electrochemically modified with gold and subsequently attached a specific DNA-based redox probe to detect low levels of miRNAs as a biomarker. Our design was applied to detect miRNA-1 (5′-TGG AAT GTA AAG AAG TAT GTA T-3′), demonstrating exceptional sensitivity and a low detection limit (LOD) of 120 fM. We also discuss how the developed platform broadly benefits numerous other miRNA sequences. Device preparation method: A screen-printed electrode (SPE) consisting of a 3 mm diameter carbon working electrode, an Ag reference electrode, and a carbon counter electrode was used as a base for device development. The bare SPE was activated during 10 cycles of cyclic voltammetry (CV) with potentials ranging from -0.5 to +1 V at a scan rate of 20 mVs-1 using PBS as the electrolyte. Before gold electrodeposition, the activated SPE was rinsed with PBS and deionized water. The electrode was submerged in a solution of 115 mM HAuCl4 (in 0.5 M H2SO4) during 10 CV cycles with the same parameters described above. Next, the DNA-probe sequence was attached to the working surface of the Au-decorated screen-printed electrode (Au-SPE). The DNA-probe sequence was modified in advance with C6-S-S-R at the 5′ end (enabling covalent binding onto Au) and methylene blue (MB) at the 3′ end (allowing electron transport at the electrode). Before modification of SPE, the disulfide bond was reduced using tris(2-carboxyethyl)-phosphine hydrochloride) (TCEP) to enable covalently attachment of the HS-DNA-MB probe to the Au-SPE [2]. The attachment of the DNA-probe sequence was achieved by placing 10 µL of the diluted probe onto the working electrode area and allowing for 1 hour of modification at room temperature to take place, as shown in Fig. 1(A). Results & Discussion: The device was evaluated for analytical performance in the presence of increasing target concentrations, as shown in Fig. 1 (A). In the absence of a miRNA-1 target, the stem-loop structure of the DNA probe remains closed, forcing the redox MB-reporter into proximity to the electrode, allowing for efficient electron transport. In the presence of miRNA-1, the target molecule binds to a complementary sequence of the DNA probe, opening the loop. The structure assumes a relatively rigid conformation where MB is farther from the gold surface, reducing the observed signal, Fig. 1 (B). The observed current gradually decreases upon the successive addition of miRNA target molecules for both cathodic, Ipc, and anodic, Iac, peaks. Using the dependence of anodic peak, Iac, the sensor was calibrated as a function of added miRNA-1 concentration in the range between 0 and 4 nM. Using the calibration curve shown in Fig. 1 (C), the detection limit was estimated to be 120 femtoM. The device was further tested for stability and exhibited robust performance over an extended period displaying minimal fluctuations in the recorded current. While we demonstrate the sensitive detection of miRNA-1, the design can be easily adapted for other miRNA sequences [3]. Additionally, it introduces exciting prospects for multiplexing, where multiple miRNA sequences can be easily detected with only minor adjustments to the established design. Fig. 1 (A) Schematics of the device fabrication process and mechanism for the development of Smart Electrochemical Sensors setup; (B) Cyclic Voltammetry (CV) responses during Au electrodeposition (black) in the absence of miRNA-1 target (red) and presence of miRNA-1 target (blue); (C) The device response curve concerning successive addition of miRNA-1 target molecule into the device chamber (different concentrations of target from 0 to ~ 4000 pM), the inset shows CV response used to plot the calibration curve. Acknowledgment: NSF DMR 2204027, US DOE EPSCoR DE-SC0024284

Low-cost multispectral sensor reveals cold chain breaks, meat type, and storage time in chicken meat samples

A portable and low-cost spectrometric system has been proposed to discriminate cold chain (frozen) interruption during the supply chain, as well as, the sample type among four different tested chicken meat and products (nugget, wing, thigh and drumstick) and the storage time elapsed after the cold chain break. To achieve this, an AS7265x sensor chipset was used and controlled by a custom programmed microcontroller through a Qwiic I2C interface. Communication between the user and the sensor setup was made using a custom programmed interface developed using the Python. Frozen chicken samples were thawed under various temperature-time combinations (4, 8, 16, and 24 h at 4 and 24 °C) and then refrozen at -18 °C. The refrozen samples were stored for up to 60 days. Spectral measurements (410–940 nm) were taken before thawing and at 1, 10, 30 and 60 days of storage after refreezing. The spectra obtained were analysed using Principal Component Analysis and outliers were eliminated using Mahalobobis distance. Several classification models (including Decision Tree, Random Forest, AdaBoost, Support Vector Machine, k-Nearest Neighbors, Gaussian-Naïve Bayes, Multilayer Perceptron, Gaussian Process, Linear and Quadratic Discriminant Analysis and Partial Least Squares Discriminant Analysis) were trained where appropriate. The top models with the highest predictive ability were selected and merged to build Soft Voting Classifiers (SVC) for each feature to be predicted. The SVC models correctly classified the samples with 93%, 92% and 83% accuracy for cold chain interruption, chicken meat/product type and storage time after thaw-refreeze cycle, respectively. In addition, a graphical interface was also developed so that it can be used by end users for food safety concerns. The results showed the potential of the developed low-cost sensor to rapidly detect the potential food safety risks due to cold chain breakage and to trace back the problem by predicting the storage time after refreezing.

Study the Effect of Ion Doping on ZnO Nanostructures for Room Temperature NH3 Gas Sensor

We investigated the impact of doping ion type on the performance of a ZnO-based ammonia gas sensor to show the capability of these ions to achieve high-performance gas sensing at room temperature. A sol-gel method was used to synthesize both doped and undoped ZnO nanostructures, while the gas sensor device was made by casting ZnO onto a glass substrate for a uniform thin film. Then Al electrodes were attached to the film. The characterization was carried out via field-emission scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction, UV–vis, Pl luminescence, Brunnauer-Emmett-Teller, I-V characteristic, and gas sensor setup device. PL measurement shows an increase in green emission spectra with Ba ion shifting the peaks from VO to VO+ and VO+ to VO++ states. The gas sensor test at room temperature greatly enhances performance for certain ions. The Ba ions greatly influence gas sensor performance, increasing the response to 24 compared to 5 for undoped ZnO. The room-temperature enhancement achieved by the Ba ions could open the way to investigate more effective dopants for NH3 gas sensors.

Design, Technical Development, and Evaluation of an Autonomous Compost Turner: An Approach towards Smart Composting

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future.

Humanactivity Recognition using Deep Learning

Human activity recognition (HAR) plays a crucial role in various fields, including healthcare, surveillance, and human-computer interaction. This study explores the application of deep learning techniques for accurate and efficient human activity recognition. Leveraging the capabilities of deep neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the proposed approach aims to capture both spatial and temporal features from sensor data. The dataset utilized in this research comprises multi-model sensor inputs, such as accelerometer and gyroscope readings, collected from wearable devices. The deep learning model is designed to automatically learn hierarchical representations of the raw sensor data, enabling robust feature extraction and discrimination between different human activities. Transfer learning is employed to enhance model generalization across diverse activity categories and varying sensor setups. Experimental evaluations are conducted on real-world datasets, demonstrating the effectiveness of the proposed deep learning framework in accurately classifying activities, including walking, running, sitting, and standing. Comparative analyses against traditional machine learning methods underscore the superior performance and adaptability of deep learning in handling complex and dynamic activity patterns. The results showcase the potential of deploying deep learning models in real-time human activity recognition systems, highlighting their scalability and efficiency. The study contributes to the advancement of HAR methodologies, paving the way for the development of more reliable and robust systems in applications such as healthcare monitoring, assisted living, and smart environments.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study.

With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.

Development and Calibration of a Microfluidic, Chip-Based Sensor System for Monitoring the Physical Properties of Water Samples in Aquacultures.

In this work, we present a compact, bifunctional chip-based sensor setup that measures the temperature and electrical conductivity of water samples, including specimens from rivers and channels, aquaculture, and the Atlantic Ocean. For conductivity measurements, we utilize the impedance amplitude recorded via interdigitated electrode structures at a single triggering frequency. The results are well in line with data obtained using a calibrated reference instrument. The new setup holds for conductivity values spanning almost two orders of magnitude (river versus ocean water) without the need for equivalent circuit modelling. Temperature measurements were performed in four-point geometry with an on-chip platinum RTD (resistance temperature detector) in the temperature range between 2 °C and 40 °C, showing no hysteresis effects between warming and cooling cycles. Although the meander was not shielded against the liquid, the temperature calibration provided equivalent results to low conductive Milli-Q and highly conductive ocean water. The sensor is therefore suitable for inline and online monitoring purposes in recirculating aquaculture systems.

Self‐Made Equipment for Automatic Methane Diffusion and Ebullition Measurements From Aquatic Environments

AbstractFreshwater ecosystems are believed to account for about half of global methane emissions, but this estimate is uncertain due to few measurements in some geographical regions and most important for our purposes, large local variations. We propose a simple, cheap (<200€), and effective solution to the urgent need for equipment that can provide accurate data on methane emission at high spatial and temporal resolution for many days, across many ecosystems and regions, and with limited inspection. A low‐cost metal oxide semiconductor sensor, placed in a floating chamber equipped with an air pump to flush the chamber's headspace at regular intervals, provides frequent and accurate high‐resolution measurements of diffusive and ebullitive methane emissions from lakes and rivers. We made several improvements that yield more useful data and enable the setup to operate for several days without visual inspection. To test the accuracy of the equipment, we compared its measurements of methane emission to parallel measurements by an expensive, commercially available laser sensor. The correspondence was excellent (R2 ≥ 0.94) and the deviation of measurements was minimal at fluxes within ranges encountered in the field. We also designed an R‐package (FluxSeparator), which associates the large sensor‐generated data sets with diffusive and ebullitive flux activity. Both the sensor setup and the R‐package are flexible and have been made publicly available to encourage others to use and further improve the equipment and calculations.

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.

Study on setting method of temperature sensor in aircraft environmental control system

This paper analyzes the working principle of each component of the aircraft environmental control system (ECS), takes the Boeing 737 aircraft as an example, and analyzes the distribution and role of temperature sensors in the aircraft ECS. Then, this paper presents a guideline for sensor setup suitable for each subsystem. The accuracy of the temperature sensor is measured, which verifies that the temperature sensor selection and set is reasonable and can ensure the good operation of the aircraft ECS.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Biomechanical load quantification of national and regional soccer players with an inertial sensor setup during a jump, kick, and sprint task: assessment of discriminative validity

Training load quantification methods may help optimize soccer performance. However, whole-body indicators potentially underestimate biomechanical load. A new inertial sensor setup allows joint-specific biomechanical load quantification. Good discriminative validity further supports the use of this method, and therefore the purpose of this study is to assess the discriminative validity of this method during soccer-specific activities. Twelve national and sixteen regional soccer players wore an inertial sensor setup and performed countermovement jumps, soccer kicks, and 30 m sprints. Between-group differences in angular acceleration-based biomechanical load indicators Knee Load, Hip Load, and performance were assessed using MANOVAs and Cohen’s effect sizes. Furthermore, relationships with performance were explored. National players showed higher Knee Load during jumping (mean difference: 0.11 A.U., ES = 0.93, p = 0.02), kicking (mean difference: 1.94 A.U., ES = 0.94; p = 0.02), and almost during sprinting (mean difference: 12.85, ES = 0.77; p = 0.05). Hip Load did not differ between groups across all tasks, although national players outperformed regional players on all tests. Significant relationships between Knee Load (rjump = 0.41, rkick = 0.65), Hip Load (rjump = 0.42), and performance were observed with 95% confidence intervals ranging from trivial to large. The results confirm discriminative validity of Knee Load for jumping and kicking, but not for sprinting and Hip Load in general. The confidence intervals of the established relationships suggest that the biomechanical loads might not entirely explain between-group differences in performance. The results can be used as reference values for biomechanical load quantification in the field.

Boosted Incremental Nonlinear Dynamic Inversion for Flexible Airplane Gust Load Alleviation

Active gust load alleviation is an important technology for reducing loads on future commercial airplanes so that the structure can be designed to be lighter in order to save fuel. For the intense reduction of gust loads, a highly reactive control system of sensors, control algorithms, and actuators is required, which is not yet available. In this paper, a control algorithm based on incremental nonlinear dynamic inversion is extended so that actuators respond faster. The control algorithm is applied to a future medium-range aircraft with distributed trailing-edge flaps. It is assumed that the vertical acceleration of the center of gravity and the displacement, velocity, and acceleration of the first wing bending mode are available as feedback variables. To allow comparability with other actuator and sensor setups, the authors abstract the actuator dynamics and sensor filter as control system time delay. The feedback gains are adjusted automatically to the control system time delay. In simulation, the authors investigate the influence of the control system time delay on the peak gust loads. They show that gust loads can indeed be intensely reduced with sufficiently small control system time delay in simulation.

Modelling Asphalt Overlay As-Built Roughness Based on Profile Transformation-Case for Paver Using Automatic Levelling System.

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative.

SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.

Auscultation is a fundamental diagnostic technique that provides valuable diagnostic information about different parts of the body. With the increasing prevalence of digital stethoscopes and telehealth applications, there is a growing trend towards digitizing the capture of bodily sounds, thereby enabling subsequent analysis using machine learning algorithms. This study introduces the SonicGuard sensor, which is a multichannel acoustic sensor designed for long-term recordings of bodily sounds. We conducted a series of qualification tests, with a specific focus on bowel sounds ranging from controlled experimental environments to phantom measurements and real patient recordings. These tests demonstrate the effectiveness of the proposed sensor setup. The results show that the SonicGuard sensor is comparable to commercially available digital stethoscopes, which are considered the gold standard in the field. This development opens up possibilities for collecting and analyzing bodily sound datasets using machine learning techniques in the future.

Multi-sensor Attitude Estimation using Quaternion Constrained GNSS Ambiguity Resolution and Dynamics-Based Observation Synchronization

Recently, high accuracy and low-cost navigation hardware is becoming increasingly available that can be efficiently used for the control of autonomous vehicles. We present a sensor fusion method providing tightly coupled integration of pseudorange, carrier phase, and Doppler satellite measurements taken at multiple vehicle-mounted GNSS antennas with onboard inertial sensor observations. The key of accurate GNSS position and orientation estimation is the successful integer ambiguity resolution. We propose a method that uses the quaternion states as constraints to improve ambiguity resolution and to increase the accuracy of the GNSS based attitude determination. Generally, the low-cost hardware neither allows a hardware-level time synchronization between the GNSS receivers due to a lack of a common external oscillator nor provides the clock steering function available in geodetic GNSS receivers. The lack of observation synchronization causes several degrees of error in attitude estimation. To eliminate this effect, a dynamics-based solution is presented that synchronizes the observations by taking the dynamics of the moving platform into account. Compared to common external oscillator based sensor setups, our solution allows to increase both the number of rover receivers on the platform and the baselines between them easily, thus it opens up new possibilities in the attitude determination of large vehicles. We validate our approach against a tactical grade inertial navigation system. The results show that our approach using low-cost sensors provides the ambiguity success rate of 100% for the moving baselines, and the positioning and attitude error reached the centimeter and half a degree level, respectively.