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2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Xinliang Zhou ◽  
Shantian Wen

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.


Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 408
Author(s):  
Jonas Chromik ◽  
Kristina Kirsten ◽  
Arne Herdick ◽  
Arpita Mallikarjuna Kappattanavar ◽  
Bert Arnrich

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.


2022 ◽  
pp. 520-536
Author(s):  
Rui Miguel Pascoal

This work analyses energy expenditure in outdoor sport environments with augmented reality technology. Battery efficiency is becoming a relevant topic in the context of the varied outdoor end-user services, among other realms. It is a key to the acceptance and use of mobile technology. In outdoor environments, battery efficiency can be low, especially when information based on close-to-real-time requires internet access and the use of sensors. Such requirement is today evident with the growth of internet dependence and multiple sensors, which perform both actively and passively via fitness gadgets, smartphones, pervasive systems, and other personal mobile gadgets. In this context, it is relevant to understand how energy is spent with the accelerometer, global position system, and internet access (Wi-Fi or mobile data) providing smart data for outdoor sports activities. Through a prototype, an analysis is made based on the current battery autonomy, and an algorithm model for better battery efficiency is proposed.


2022 ◽  
Vol 10 (1) ◽  
pp. 0-0

Along with artificial intelligence technologies, deep learning technology, which has recently received a great deal of attention, has been studied on the basis of developed artificial neural networks. This thesis deals with the detection, recognition, judgment, and control that are included in the basic technologies of the autonomous driving subsystems to achieve fully autonomous driving. And this work solves many problems in this area. The use of the CARLA simulation in this project is the development of a deep learning intelligent autonomous driving system in the road environment. Autonomous driving recognizes the situation by processing the data collected through images from multiple sensors or lidars and cameras in real-time. In the cloud server process using real data, explore various deep learning models for traffic flow prediction, return the model trained onboard, perform the prediction and solve the problem of fully autonomous driving, including a module of control, which is a CARLA simulation.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chaoyong Shen ◽  
Zongjian Lin ◽  
Shaoqi Zhou ◽  
Xuling Luo ◽  
Yu Zhang

Multisource remote sensing data have been extensively used in disaster and emergency response management. Different types of visual and measured data, such as high-resolution orthoimages, real-time videos, accurate digital elevation models, and three-dimensional landscape maps, can enable producing effective rescue plans and aid the efficient dispatching of rescuers after disasters. Generally, such data are acquired using unmanned aerial vehicles equipped with multiple sensors. For typical application scenarios, efficient and real-time access to data is more important in emergency response cases than in traditional application scenarios. In this study, an efficient emergency response airborne mapping system equipped with multiple sensors was designed. The system comprises groups of wide-angle cameras, a high-definition video camera, an infrared video camera, a LiDAR system, and a global navigation satellite system/inertial measurement unit. The wide-angle cameras had a visual field of 85° × 105°, facilitating the efficient operation of the mapping system. Numerous calibrations were performed on the constructed mapping system. In particular, initial calibration and self-calibration were performed to determine the relative pose between different wide-angle cameras to fuse all the acquired images. The mapping system was then tested in an area with altitudes of 1000 m–1250 m. The biases of the wide-angle cameras were small bias values (0.090 m, −0.018 m, and −0.046 m in the x-, y-, and z-axes, respectively). Moreover, the root-mean-square error (RMSE) along the planer direction was smaller than that along the vertical direction (0.202 and 0.294 m, respectively). The LiDAR system achieved smaller biases (0.117, −0.020, and −0.039 m in the x-, y-, and z-axes, respectively) and a smaller RMSE in the vertical direction (0.192 m) than the wide-angle cameras; however, RMSE of the LiDAR system along the planar direction (0.276 m) was slightly larger. The proposed system shows potential for use in emergency response systems for efficiently acquiring data such as images and point clouds.


Author(s):  
Mehmet Ali Dincer ◽  
Kubra Evren Sahin ◽  
Savas Sahin

In this study, the development of a low-cost electronic card-based medical device measuring and recording patient data was described via non-invasive methods. Both the descriptive statistical analysis and the regression model was performed from the pulse and galvanic skin response (GSR) from the volunteer' data. It is important to measure and record different data simultaneously with multiple sensors from the patient during the treatment, medical operation and care periods of the patients. The data measured from the designed device was evaluated for the patient's position, GSR, the respiration rate, the blood oxygen content, and the heart rate. The designed measurement and recording device were implemented with an embedded system-based microcontroller card. The designed device might provide for monitoring and recording data with led display, serial port, microSD card or internet of things.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 144
Author(s):  
Alexander Genser ◽  
Noel Hautle ◽  
Michail Makridis ◽  
Anastasios Kouvelas

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.


2021 ◽  
pp. 1-13
Author(s):  
Marina Mohd Nor ◽  
Norzailawati Mohd Noor ◽  
Sadayuki Shimoda

The deterioration of streets in the historical city of Malacca in Malaysia due to modernization contributes to the streets’ vulnerabilities. This paper purposely analyses the physical transformation of the street networks for the years of 1993-2015, and the cultural influences and impact throughout the establishment of multi-racial cultural society. The methodology for the study is through mapping the street networks of Malacca city by using SPOT satellite imageries of three different years; 1993, 2005, and 2015, and through the street semi-automatic extraction technique to monitor the street pattern of Malacca city. Multiple sensors of SPOT were used, consisting of SPOT-2XS, SPOT 5, and SPOT 6 with 20 m, 5 m, and 1.5 m resolutions in extracting the street objects, while using the IMAGINE OBJECTIVE tools from ERDAS. The finding shows that the street network trend varied from 1993, 2005, and 2015 where the streets achieved 23.8% street expansions in the year 1993 compared to 10.49% in the year 2005. However, the development trend of streets increased to 14.68% in the year 2015. The connection of the physical transformations of the streets with the cultural impact contributed to the sense of place and divided the streets based on socio-economic, cultural and ethnic lines. Finally, it shows that the trend and pattern of street networks were essential in understanding a city’s morphology that has a significant impact on cultural evolution since the establishment of the Chinese community in Malacca.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8466
Author(s):  
Quanxing Wan ◽  
Benjamin Brede ◽  
Magdalena Smigaj ◽  
Lammert Kooistra

The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor’s actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.


2021 ◽  
Author(s):  
Miguel Gonzalez ◽  
Tim Thiel ◽  
Chinthaka Gooneratne ◽  
Robert Adams ◽  
Chris Powell ◽  
...  

Abstract During drilling operations, measurements of drilling fluid/mud viscosity and density provide key information to ensure safe operations (e.g., maintain wellbore integrity) and improve the rate of penetration (e.g., maintain proper hole cleaning). Nowadays, these measurements are still performed manually by using a calibrated funnel viscometer and a weight balance, as stipulated by current American Petroleum Institute (API) standards. In this study, we introduce an automated viscosity/density measurement system based on an electromechanical tuning fork resonator. The system allows for continuous measurements as fast as several times per second in a compact footprint, allowing it to be deployed in tanks or pipelines and/or gathering data from multiple sensors in the mud circulation system. The streams of data produced were broadcasted to a nearby computer allowing for live monitoring of the viscosity and density. The results obtained by the in-tank system in five wells were in good agreement with the standard reference measurements from the mud logs. Here, we describe the development and testing of the tool as well as general guidelines for integration into a rig edge-computing system for real-time analytics and detection of operational problems and drilling automation.


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