Data Acquisition Device Research Articles

Prediction and classification of sEMG-based pinch force between different fingers

The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand.

An edge-computing framework for operational modal analysis of offshore wind-turbine tower

With massive construction of offshore wind turbines (OWT), structural health monitoring (SHM) techniques have been applied to estimate the working conditions and provide early warnings of structural damage. This study proposes an edge-computing-based SHM system for operational modal analysis (OMA) of an OWT. ThssCe system comprised an open-source data acquisition (DAQ) device, a Raspberry Pi single-board computer, and associated drivers, protocols, and algorithms. The DAQ device can measure the acceleration response of an operating OWT subjected to environmental excitation and transmit raw signals to the Raspberry Pi according to the UDP protocol. The data are further processed to obtain the first-order frequency via an OMA algorithm developed by combining Kalman filtering, the random decrement technique (RDT), and the stochastic subspace identification (SSI) method. The proposed SHM system was implemented in a scaled OWT model to verify its feasibility for field use. The experimental results indicate that (1) the edge-computing-based SHM system can accurately measure the response of the OWT; (2) the proposed algorithm can estimate the modal information of the OWT during operation; (3) the variation in the first-order frequency induced by lateral loads can be identified by the SHM system. Unlike a centralized SHM system, the proposed edge-computing-based SHM system does not require massive data transmission, making it promising for offshore environments.

Development and testing of a ground recognition system for tractor field operations

To overcome the problems of poor recognition accuracy, slow response speed, and unsuitable for farmland ground environment based on the traditional ground recognition method of vehicle mathematical model, this paper proposes a ground recognition system for tractor field operation based on wheel acceleration. A wheel acceleration sensor and data acquisition device are designed, and a ground recognition algorithm based on Principal Component Analysis - Genetic Algorithm optimization of an Extreme Learning Machine (PCA-GA-ELM) is investigated. The system can collect acceleration samples and extract statistical features in different frequency bands in real time according to the cycle of wheel rotation and then use the trained recognition model to recognize the ground type. In the actual operation process, this system only needs to collect the acceleration test data of one rotation of the tractor wheels to achieve the recognition of the ground. The test set data show that this system achieves 96 % discrimination accuracy, higher than the 87 % of the ELM model without genetic algorithm optimization and 92 % of the GA-BP neural network model. In addition, the real-time ground recognition test shows that the increase in operation speed will slightly reduce the accuracy of the recognition model, but the overall can still maintain 94–98 % accuracy, indicating that the system has good stability. This system can provide a reference for real-time speed adjustment, implement position, and other control parameters during tractor operation.

Open source framework for a Broadly Expandable and Reconfigurable data acquisition and automation device (BREAD)

Though open source data acquisition (DAQ) systems have been published, closed source proprietary systems are the standard despite often being prohibitively expensive. High costs, however, limit access to high-quality DAQ in low-resource settings. In many cases the functions executed by the closed source and proprietary DAQ cards could be carried out by an open source alternative; however, as desired function count increases, the simplicity of integrating the designs decreases substantially. Although the global library of open source electronic designs is expanding rapidly, and there is clear evidence they can reduce costs for scientists one device at a time, they are generally made to carry a function well, but are often not capable of scaling up or easily being integrated with other designs. Just as other open source projects have found success by having modular frameworks and clearly documented specifications, a framework to unify and enable interoperation of these open source electronics systems would be greatly beneficial to the scientific community. To meet these needs and ensure greater accessibility to high-quality electronics sensing and DAQ systems, this article shares and tests a news framework where new open source electronics can be developed and have plug-and-play functionality. The Broadly Reconfigurable and Expandable Automation Device (BREAD), consists of a basic set of guidelines and requirements to which others can contribute. Here 7 slices (boards) are provided, demonstrated, and validated: 1) Amplified Analog Input, 2) Audio Analysis / Fourier Transform, 3) +/- 10A Current Sensor, 4) 4-Channel Relay Controller 5) 4 Channel Stepper Motor Controller, 6) 4 Channel Type-K Thermocouple Reader and 7) 2 Channel USB Port. Implementing systems using BREAD rather than closed source and proprietary alternatives can result in cost savings of up to 93%.

Fringe-to-pulse counting demodulation of a laser interferometer for primary vibration calibration

A high accuracy and real-time amplitude-phase demodulation method based on the pulse counting of optical fringes is proposed for a laser interferometer to realize amplitude and phase measurement of the vibration in primary vibration calibration. The two orthogonally analog signals with high frequency are converted into two digital signals through the comparison module instead of a high-speed data acquisition device. Then one displacement increment during a quarter period of the digital signal can be obtained by the high-frequency clock through comparing the four states of two orthogonally digital signals. The actual output rate of the demodulation data will be decreased by subsample with the vibration frequency. The results of experiment illustrated the deviations were within the limits and the displacement waves were well reconstructed, not only for homodyne interferometer but also for heterodyne interferometer. The proposed method involves less complicated hardware equipment and algorithm, so it is sufficient for real-time and high-precision demodulation of the laser interference signals to realize vibration measurements in primary vibration calibration.

Study of Wind Potential on the La Bella Farm in Rural Area of the Municipality of Acevedo (Huila, Colombia)

Purpose: This project consisted of a technical study of the location of a wind microgeneration system and the collection of data that impact on said system at the La Bella farm in a rural area of the municipality of Acevedo (Huila, Colombia). Theoretical framework: The Weibull distribution was used to statistical analysis of the data, which is the most widely used system for calculating wind potential. Method/design/approach: The study was divided into two phases "previous study" and "intensive study", 95,760 data were collected in total and were statistically analysed using Weibull frequency distribution and WAsP software. Results and conclusion: The average annual speed of 5.7 and 6.5 m/s, these being higher than the speed necessary to obtain an electric potential. Research implications: It is concluded that the wind potential is 135.2 W/m2 and 186.2 W/m2 respectively. Originality/value: The anemometer data acquisition device is an original design assembled for this study. The statistical analysis of the wind using the Weibull distribution was programming like mathematical model in Microsoft Excel and compared with the results obtained by the WAsP Climate Analyst software.

Real-Time Speed Monitoring of Elevator System Based on Low-Cost IMU

Condition monitoring techniques are widely used in various industries to increase efficiency and reduce maintenance costs, but it has yet to be extensively researched in elevator system. In this paper, we propose a model-based method with high accuracy and strong robustness for real-time speed monitoring in elevator system using low-cost inertial measurement unit (IMU) as the data acquisition device. The algorithm uses attitude correction, Kalman filter within overlapping sliding window, and zero-velocity update to eliminate the interference, including the low precision and random placement angle of IMU, different motion characteristics of the elevator, and slight vibration caused by the external environment. The experimental results indicate that the performance of the algorithm on the test sets can be comparable to that of classical supervised learning models and outperform the direct integration method. Subsequent experiments have verified that the proposed method can maintain high accuracy for a long time in real scenarios.

Video-based Elevated Skin Temperature Detection.

In this work, we propose a non-contact video-based approach that detects when an individual's skin temperature is elevated beyond the normal range. The detection of elevated skin temperature is critical as a diagnostic tool to infer the presence of an infection or an abnormal health condition. Detection of elevated skin temperature is typically achieved using contact thermometers or non-contact infrared-based sensors. The ubiquity of video data acquisition devices such as mobile phones and computers motivates the development of a binary classification approach, the Video-based TEMPerature (V-TEMP) to classify subjects with non-elevated/elevated skin temperature. We leverage the correlation between the skin temperature and the angular reflectance distribution of light, to empirically differentiate between skin at non-elevated temperature and skin at elevated temperature. We demonstrate the uniqueness of this correlation by 1) revealing the existence of a difference in the angular reflectance distribution of light from skin-like and non-skin like material and 2) exploring the consistency of the angular reflectance distribution of light in materials exhibiting optical properties similar to human skin. Finally, we demonstrate the robustness of V-TEMP by evaluating the efficacy of elevated skin temperature detection on subject videos recorded in 1) laboratory controlled environments and 2) outside-the-lab environments. V-TEMP is beneficial in two ways; (1) it is non-contact-based, reducing the possibility of infection due to contact and (2) it is scalable, given the ubiquity of video-recording devices.

Integrating Cyber Resilience Strategies for OT Data Acquisition Devices

Operational Technology (OT), Industrial Control Systems (ICS), and Supervisory Control and Data Acquisition (SCADA) devices have been around in some form since the 1960s and they control specific functions in many critical industries such as electrical power generation, oil refineries, and water treatment plants. Since most are integrated with IT-based protocols (e.g., TCP/IP), there has been an explosion of OT control systems to provide meaningful information to businesses. While OT and IT speak a common language, this has exposed OT to much more Cyber Attacks that were once applied only to IT-based systems. Data Acquisition Devices (DAS), such as those manufactured by Moxa, now have ethernet ports to provide this link between both networks. The combination of new ethernet-capable OT devices, and those using upgraded converters (e.g., Moxa) for older OT devices, has primarily increased the attack surface. This study proposes a secure layered architecture that can be deployed to limit security threats for ethernet-capable (DAS) devices. The current state of many organizations is the lack of visibility of their OT assets and a knowledge gap on how to secure them. Keywords—Control systems, Cyber attacks, Cybersecurity resilience, Operational technology

Charge Couple Device (CCD) Photoelectric Signal Data Acquisition and Its Application in the Machine Vision of Artificial Intelligence

The development of Charge Couple Device (CCD) technology is particularly rapid in the fields of image sensors and non-contact measurement. In this study, a data acquisition device applied to CCD photoelectric detection system is designed. Among them, the design of the Differential Amplification (DA) module, Analog-to-Digital Converter (ADC) module, First In First Out (FIFO) cache module, and Complex Programmable Logic Device (CPLD) module in this device are emphasized. The ADC circuit in the ADC module converts two 4 MHz analog photoelectric signals generated by the CCD sensor at a frequency of 8 MHz, and then outputs 12-bit digital signals. The collected photoelectric signal is used to detect the damage to the surface of ancient buildings with the machine vision technology of artificial intelligence (AI). In the test, the DA circuit can adjust the voltage range of two photoelectric analog signals output by CCD to a predetermined range (1.5 V∼2.0 V). In the ADC circuit test, there is no data in the FIFO when there is no input conversion, and the converted data will be stored in the internal FIFO during the conversion clock period. Based on machine vision technology, surface damage types of ancient buildings are defined, namely spalling, cracks, and disruption, and surface image samples are generated from collected signals. The samples are trained using the convolutional neural network, and the classifier is generated. The test reveals that the designed photoelectric signal acquisition device and AI machine vision technology can accurately classify the surface damage of ancient buildings.

Additive Manufacturing in Bespoke Interactive Devices—A Thematic Analysis

Additive Manufacturing (AM) facilitates product development due to the various native advantages of AM when compared to traditional manufacturing processes. Efficiency, customisation, innovation, and ease of product modifications are a few advantages of AM. This manufacturing process can therefore be applied to fabricate customisable devices, such as bespoke interactive devices for rehabilitation purposes. In this context, a two-day workshop titled Design for Additive Manufacturing: Future Interactive Devices (DEFINED) was held to discuss the design for AM issues encountered in the development of an innovative bespoke controller and supporting platform, in a Virtual Reality (VR)-based environment, intended for people with limited dexterity in their hands. The workshop sessions were transcribed, and a thematic analysis was carried out to identify the main topics discussed. The themes were Additive Manufacturing, Generative Design Algorithms, User-Centred Design, Measurement Devices for Data Acquisition, Virtual Reality, Augmented Reality, and Haptics. These themes were then discussed in relation to the available literature. The main conclusion of this workshop was that a coherent design for AM tools is needed by designers to take AM considerations throughout the design process, since they lack the AM knowledge required to develop bespoke interactive devices.

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

Building a high-precision bathymetry digital elevation model is essential for navigation planning, marine and lake resource planning, port construction, and underwater archaeological projects. However, existing bathymetry methods have yet to be effectively and comparatively analyzed. This paper comprehensively reviews state-of-the-art bathymetry methods, including data acquisition techniques, model accuracy, and interpolation algorithms for underwater terrain mapping. First, We assess the merits and drawbacks of novel data acquisition devices, such as single-beam/multi-beam echo sounders and light detection and ranging systems. After that, we analyze the accuracy of the ETOPO1, GEBCO_2022 and SRTM15 to provide valuable insights into their performance. Furthermore, we evaluate ANUDEM, Inverse Distance Weighting, Kriging and Nearest Neighbor interpolation algorithms in different underwater terrains by comparing their applicability, reliability, and accuracy in various underwater environments. Finally, we discuss the development trends and challenges in underwater bathymetry technology and offer a forward-looking perspective on the future of this essential field.

Educational interactive virtual strain and consolidation tests using LabVIEW and Arduino Uno microcontroller

AbstractThe main objectives of this research are to derive a modified retardation time parameter of the Kelvin model in terms of the coefficient of consolidation of the 1D‐consolidation equation and to design and implement an educational LabVIEW program linked with Arduino‐Uno microcontroller as data acquisition devices for practical verification of the results inside the classroom. Consolidation coefficients of the derived equation decrease with increasing retardation time parameters of the Kelvin model. The regression module of the educational program shows a negative power of fit with an MSE of (0.134), and a 1:1 scatter plot between the derived equation and the 1D‐consolidation equation, indicating a complete fit between both equations. The derived negative power fit equation of the educational program has led to a new expression of the stress ratio of the dashpot unit of the Kevin model. The altered Kelvin dashpot stress ratio is verified experimentally using a resistance‐capacitance (RC) circuit connected to the microcontroller and linked with the educational program. The RC circuit, Arduino‐Uno, and the LabVIEW educational program are “useful” tools in teaching the rheology of soil mechanics inside the classroom. The practicality of the RC circuit for simulating the rheology of soil mechanics inside the classroom enhances the student's understanding and improves their standards in the rheology of soil mechanics.

Difference analysis of musculation and estimation of sEMG-to-force in process of increasing force and decreasing force

The movement of the human limb is driven by muscle contraction. Surface electromyography (sEMG) is a weak bioelectric activity generated during muscle contraction, which reflects information regarding muscle activation. The increasing force and the decreasing force are reverse processes. Investigating the difference in musculation between the two processes and establishing an input–output model between sEMG and force can clarify the biodynamics mechanism of the human body. In the study, we try to find the truth about the difference in musculation using sEMG signal in the process of increasing and decreasing force, and create a model of the relationship between sEMG and force. A synchronous data acquisition device is used to collect force and sEMG signals, including the raw sEMG signal and its envelope signal. A new method for extracting the feature of the sEMG signal based on the spectrogram is introduced. Up to sixteen features are extracted from the sEMG signal, and their performances are evaluated. The experimental results indicate that sliding mean filtering can significantly improve feature performance. A processing means of isometric force and sEMG feature is proposed. Difference in musculation about force-increasing and force-decreasing is detailedly analyzed by statistical T-test. We come to the conclusion that the sEMG signal evoked via musculation is not exactly the same in the two processes, with a more significant difference when the muscle contraction strength is weaker, and a less significant difference when the muscle contraction strength is stronger. Finally, five regression models are used for sEMG-to-force estimation, and their performances are compared separately. The experimental results show that the DNN exhibits the best performance, achieving a RMSE of 12.782 and a R2 of 0.911.

EEG-Based Driver Fatigue Monitoring within a Human–Ship–Environment System: Implications for Ship Braking Safety

To address the uncontrollable risks associated with the overreliance on ship operators’ driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human–ship–environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human–ship–environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.

Graphene-Based Smart Insole Sensor for Pedobarometry and Gait Analysis

Pedobarography and gait analysis are crucial components of healthcare, given their close association with chronic illnesses. Wearable electronics, such as smart insole systems, have emerged as a promising technology for remote and long-term gait monitoring, providing more accurate data in real time. To meet the demands of wearable electronics, components with both flexibility and sensitivity are crucial. This study employed low-cost and rapid prototyping techniques to develop a flexible piezoresistive pressure sensor with a graphene nanoplatelet/ polycaprolactone composite spray-deposited on a fiber-based commercial insole. The sensor demonstrated a pressure range of up to 400 kPa and high linear sensitivity (∼ 0.376 kPa-1), making it suitable for pedobarography. Additionally, to test its practical application, the sensor was linked to a low-power programmable data logging and transmitting device for data acquisition over custom designed android application. The sensor can differentiate various motion states, such as walking, jumping, and running, while also demonstrating potential for long-term stability.

Perancangan dan Pembuatan Data Acquisition Device Sebagai Sistem Akuisisi Data untuk Kendali Mobil Formula Student

Data Acquisition Device (DAQ) is an electronic component used in formula student vehicles. To optimize the performance of the formula student vehicle and its driver, it is necessary to analyze and monitor the data acquisition system. Parameters acquired on the car include the position of the brake pedal/throttole and wheel speed.DAQ system has 5 input channels namely 3 analog input pins and 2 digital input pins, and 3 output channels, which is the controller pin, fault pin, and brake light pin. The DAQ system in this research is designed and made using Teensy 3.6, a signal conditioning circuit consisting of an RC low pass filter, voltage follower, non-inverting amplifier, and logic level shifter. DAQ system uses CANBUS to read and process sensor data. DAQ system can acquire data from the KTC Linear Motion Position sensor PZ-12-A-50P with an accuracy value of 99,91%; Hall-effect Rotary Position sensor RTY120LVNAX with an accuracy value of 99,94% for both the first and second sensors; and Proximity sensor LJ12A3-4-Z/BX with an accuracy value of 99,58% for the first sensor and 99,46% for the second sensor. DAQ is able to run controller signal processing, detect faults, and activate brake light signal according to FSAE rules.

Small Size and Low‐Cost TENG‐Based Self‐Powered Vibration Measuring and Alerting System

AbstractVibration measurement systems containing sensors, signal conditioning, and data acquisition devices, are important for monitoring motors, gearboxes, turbines, etc. Microelectromechanical and piezoelectric sensors are predominantly used for vibration measurements. However, they are not cost‐effective, flexible in design, and incapable of self‐powering. Recently, triboelectric nano‐generator (TENG)‐based vibration sensors have been considered as a possible alternative to resolve this problem, and tremendous progress has been achieved. Previous work on TENG‐based sensors is limited to optimizing the sensor design, while the signal conditioning and data acquisition of TENG signal still need investigation for actual applications. This work develops a TENG‐based vibration measurement device and self‐powered alerting system that is integrated with the signal condition and data acquisition systems. The experimental results show that the proposed measurement system successfully measures signals within the range of 0–1800 Hz frequency. Meanwhile, the TENG generates a high output, up to 80 V and 0.55 µA from small size TENG area (3.6 cm2). The signal is adequate to harvest energy for self‐powering to drive alerting components (harvest 320 mJ in 36 h, which drives alarming for duration of 1.5 s). The proposed device is cost‐effective (30 $), small (105 cm3), and consumes less power (0.18 W) in comparison to commercial devices.

A Current Spectrum-Based Algorithm for Fault Detection of Electrical Machines Using Low-Power Data Acquisition Devices

An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. The general wards of two large hospitals, one an academic medical center and the other a community hospital. The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. None. The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.