Multi-sensor Data Acquisition Research Articles

A gait phase recognition method for obstacle crossing based on multi-sensor fusion

The analysis of human motion patterns and gait phase detection holds significant importance for both human motion analysis and precise control of exoskeleton devices. Presently, research on gait during obstacle crossing primarily focuses on the medical rehabilitation domain, with limited studies concerning gait phase analysis during obstacle crossing. This study conducts a detailed analysis of gait data during obstacle crossing and proposes a CNN-PCA-LSTM algorithm that fuses multi-sensor data to accurately identify gait phases when crossing obstacles in the horizontal direction. A wearable multi-sensor data acquisition system was designed, utilizing flexible capacitive pressure sensors to collect pressure data from the soles of both feet and IMUs to gather foot motion data. Kinematic simulation analyses of human obstacle-crossing motions were performed using Anybody software to determine various gait phases, validating the efficacy of the simulation model. Subsequently, one-dimensional and two-dimensional convolutional neural networks were separately employed to extract features from sole pressure data and foot motion data. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of these feature datasets, which were then inputted into LSTM networks. Finally, the Softmax function was employed for the classification and recognition of gait phases when crossing obstacles in the horizontal direction. The experimental results indicate that employing the CNN-PCA-LSTM algorithm integrating multiple sensor data achieves a recognition accuracy of 97.91 % for identifying gait phases during horizontal obstacle crossing.

Multi-sensor fusion based industrial action recognition method under the environment of intelligent manufacturing

In the context of intelligent manufacturing and Industry 4.0, the manufacturing industry is rapidly transitioning toward mass personalization production. Despite this trend, the assembly industry still relies on manual operations performed by workers, considering their cognitive ability and flexibility. Thereinto, studying operator action perception and recognition methods is a vital filed and of great significance for improving the production efficiency and ensuring product quality. In this paper, a multi-sensor fusion-based data acquisition system is constructed to address the challenge of achieving comprehensive and accurate perception of the assembly process with a single sensor. Then, an action recognition model architecture based on ResNet + LSTM + D-S evidence theory is proposed and established. By fully considering the characteristics of different data, the multi-sensor data values are maximized, data complementarity is achieved, and the recognition accuracy exceeds 97 %. This research is expected to provide guidance for increasing the degree of workshop automation and improving the efficiency and quality of the production process.

Moisture content online detection system based on multi-sensor fusion and convolutional neural network.

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

Research on the Application of Multi-Sensor Data Acquisition Technology in the Internet of Things (IoT) Field

Research on the Application of Multi-Sensor Data Acquisition Technology in the Internet of Things (IoT) Field

Research on Human Gait Phase Recognition Algorithm Based on Multi-Source Information Fusion

Gait phase detection is of great significance in the field of motion analysis and exoskeleton-assisted walking, and can realize the accurate control of exoskeleton robots. Therefore, in order to obtain accurate gait information and ensure good gait phase detection accuracy, a gait recognition framework based on the New Hidden Markov Model (NHMM) is proposed to improve the accuracy of gait phase detection. A multi-sensor gait data acquisition system was developed and used to collect the training data of eight healthy subjects to measure the acceleration and plantar pressure of the human body. Accuracy of the recognition framework, filtering algorithm and window selection, and the missing validation of the generalization performance of the method were evaluated. The experimental results show that the overall accuracy of NHMM is 94.7%, which is better than all other algorithms. The generalization of the performance is 84.3%. The results of this study provide a theoretical basis for the design and control of the exoskeleton.

Analysis of Regional Economic Development Trends and Strategy Formulation Based on Sensor Data Collection

In this paper, a multisensor data acquisition system based on FPGA is designed to provide an overview of the current development of the company based on the theoretical knowledge of enterprise development strategy. Through a comprehensive investigation of the company’s internal environment, the advantages and disadvantages of the company at this stage are clarified, and the opportunities and threats to the company’s development are analyzed in the context of the external macroenvironment and industry development. The coupling coordination degree from 2011 to 2025 was measured using the coupling coordination degree model based on the original data and the prediction results, and the results showed that from 2011 to 2025, the coupling coordination degree of Hebei Province showed a continuous growth and experienced the transition from low coordination, basic coordination, to medium coordination in the coupling coordination stage, aiming at the most innovative fields, mastering the most advanced technologies, cooperating with industry giants, innovating, and building digital ecology, creating, and forming core competitiveness based on core independent products and platform-based solutions, and further promoting transformation and upgrading and high-quality development. In this paper, the data acquisition system is tested by building an experimental platform. The main purpose is to test the stability and accuracy of each module of the data acquisition system. After each module test of the system meets the design requirements, the parallel mechanism attitude transformation test experiment is completed, including the moving platform moving test experiment and the moving platform rotation test experiment. The experimental results show that the designed data acquisition system has stable program and reliable function.

Rehabilitation robot following motion control algorithm based on human behavior intention

In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids. This paper proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient’s movement. Firstly, a multi-sensor data acquisition system is designed according to the rehabilitation needs of the patient and the movement characteristics of the human body. A mathematical model of movement behavior is then established. By analyzing and processing motion data, the change in the center of gravity of the human body and the behavior intention signal are derived and used as a control command for the robot to follow the human body’s movement. Secondly, in order to improve the control effect of rehabilitation robot following human motion, an adaptive radial basis function neural network sliding mode controller (ARBFNNSMC) is designed based on the robot dynamic model. The adaptive adjustment of switching gain coefficient is performed by radial basis function neural network. The controller can overcome the influence caused by the change of robot control system parameters due to the fluctuation of the center of gravity of human body, enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human body motion. Finally, the motion following experiment of the rehabilitation robot is performed. The experimental results show that the robot can recognize the motion intention of human body and perform the training goal of following different subjects to complete straight lines and curves. The correctness of human motion behavior model and robot control algorithm is verified, which shows the feasibility of the intelligent control method proposed in this paper.

A Digital Twin-Based Operation Status Monitoring System for Port Cranes.

To address the problems of the lack of an online data simulation test environment, the poor openness of data collection, and the low degree of data visualization in the online control process of port cranes, an operation state monitoring system framework for port cranes based on digital twins is proposed. In this framework, the digital twin port crane is used as the core, and the multi-sensor data acquisition method, OPC UA information model, and plug-in programming method are combined to realize multi-source heterogeneous virtual and real data fusion. The digital twin crane monitoring system based on this framework can fulfil the following functions: crane historical operation process reproduction, control program simulation testing, synchronous mapping simulation, and remote control. In order to verify the proposed method, a digital twin-based physical platform for monitoring a rail-mounted gantry crane (RMGC) was built, in which a virtual test of anti-swing control and a digital twin monitoring experiment were carried out. The results show that the virtual RMGC can test the control algorithm and map the movement process of the physical RMGC, and the crane operation monitoring system has high real-time performance and good visualization effect. In addition, the remote control of the software platform is accurate and effective.

Research and development of intelligent safety sensor integration devices for autonomous driving

Grasping the status of vehicles timely and accurately is the key to avoid traffic accidents and collecting data from multi-sensors is significant for development of road-side intelligent sensor technology. In this paper, LiDAR (Light Detection and Ranging) and camera sensors are tested, and the sensor data can be processed and analyzed, and the display can be integrated. In this paper, a multi-sensor data acquisition integration device based on Raspberry Pi is proposed, which can realize the optimal processing of data and simultaneous acquisition display function. This method avoids the tedium of using an IPC and improves the efficiency of data acquisition and sensor integration.

A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling.

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

REMOTE SENSING AND CITY INFORMATION MODELING FOR REVEALING THE COMPLEXITY OF HISTORICAL CENTERS

Abstract. Historical centers represent the outcome of transformations and stratifications of the cities across the centuries. The knowledge of a historical urban environment requires an analytical methodology articulated on several interconnected levels of investigation to model a multi-layered complexity that encompasses the geometric and stylistic features of places (blocks irregularities, narrow streets, stratified buildings), the accessibility (pedestrial zone, no flyzone), the use of existing data (GIS, cartographies). Today the challenge for historical centers is dual: on the one side to make use of expeditious technologies to acquire data, on the other one to create 3D city models that allow to manage, visualize, enquire and use these data in a unique digital ecosystem. Our research deals with a multi-sensor data acquisition, evaluation and integration with the aim of creating informed and responsive 3D city models (CIM) that constitute a synthesis of the survey conducted and become the support for simulations in various contexts (seismic risk, hydraulic, energy performance).

Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model

Abstract Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes.

Gait Stabilization Control Method of Machine Leg Based on Multi Sensor Tracking Fusion

In the course of walking, the machine leg is disturbed by the parameters and the influence of the mechanical parts, which leads to the poor stability of the walking. In order to improve the gait stability of the robot leg, a gait stability control method based on multi-sensor tracking fusion is proposed. Multi-sensor data acquisition method is used to collect the attitude information of the machine leg, fusion of attitude parameters is taken based on Kalman filtering algorithm, the fusing attitude information of the robot leg is input into the time-delay damping controller to compensate the error of the attitude parameter in the motion of the robot leg. The feedback control of the walking gait of the machine leg is realized and the adaptive control method is used to improve the stability control performance of the gait of the machine leg. The simulation results show that this method is effective for the gait stability control of machine legs, and the output of machine legs is stable and accurate, which improves the stability and robustness of machine legs.

An UAS-assisted multi-sensor approach for 3D modeling and reconstruction of cultural heritage site

Abstract Unmanned Aerial System (UAS) has been widely used to produce highly-precise orthomosaics, Digital Surface Models (DSMs), Digital Terrain Models (DTMs) and 3D models in many applications. UAS is also utilized to document cultural heritage sites using low-cost photogrammetric approach. Particularly, possibility of multi-sensor acquisition provides substantial information about both geometric features and material classification. In this study, a novel methodology using multi-sensor data acquisition is proposed in order to extract and to distinguish material features from UAS-based photogrammetry for the cultural heritages. Sensors which are able to collect visible, thermal and infrared radiations of the electromagnetic spectrum were employed to produce 3D model information of Assus Ancient Theater located in Behramkale Village, Canakkale, Turkey. The results showed that the accuracies of the 3D models were obtained as ±2–3 cm, ±10–15 cm and ±5–7 cm for the digital, thermal and multi-spectral camera systems, respectively. Beside the given high-accurate geometric model, the classification outcomes as a result of the spectral analysis revealed material features in an affordable and efficient way.

Design and Implementation of Fall Detection System Based on ZigBee

Elderly people are unable to be rescued promptly when they fall down from the chair or bed, and may be injured seriously. A fall detection system for chair based on ZigBee was investigated in this paper for this problem. Gotten the fallen information by multi-sensor data acquisition and detection technology, uploaded to PC or Android terminals through ZigBee-WiFi gateway, and sent the information to mobile phone in SMS through GSM module. The adaptive weighted fusion algorithm was used to improve the accuracy of monitoring-data. The results show that one who take the system can get the alert message instantly by PC, Android terminals and mobile phones, so it has the potential to satisfy the application of the elderly peoples care problem.

Lab-on-Chip Prototype Platform for Ochratoxin A Detection in Wine and Beer

We describe the development of a micro total analysis system for the detection of ochratoxin A (OTA) in wine and beer. The OTA detection strategy is based on an indirect competitive ELISA detection by chemiluminescence with the integration of microfluidics with microfabricated amorphous silicon photodiodes fabricated on glass substrates [1] . The full lab-on-chip (LoC) assay is implemented on a prototype device for fully-automatic measurement and monitoring of photodiode signals and control of fluid handling. The device is composed of three peripheral control and measurement modules: a peristaltic pump, a fluid dispenser for fluid handling, and a multi-sensor data acquisition with custom transimpedance amplifier. A microcontroller interfaces the individual modules with a computer via USB communication. The analysis is run automatically and the results displayed through a PC controlled user interface. OTA was detected in wine and beer extracts down to 0.5 ng/mL.

Multi-sensor driver drowsiness monitoring

A system for driver drowsiness monitoring is proposed, using multi-sensor data acquisition and investigating two decision-making algorithms, namely a fuzzy inference system (FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver. Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental set-up of a driver-drowsiness-monitoring system is designed on the basis of the sought-after indicator signals. These selected signals are the eye closure via pupil area measurement, gaze vector and head motion acquired by a monocular computer vision system, steering wheel angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted. For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are involved in a standard highway simulation for 1.5h, giving a data set of 30 pairs. For designing a feature space to be used in decision making, several metrics are derived using histograms and entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness level. To construct the rule base of the FIS, two different methods are employed and compared in terms of performance: first, linguistic rules from experimental studies in literature and, second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels belonging to each session are determined by the participants before and after the experiment, and videos of their faces are assessed to obtain the ground truth output for training the systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states (training set) and 89 per cent of previously unknown test set states, while the ANN has a correct classification rate of 90 per cent for the test data. No significant difference is observed between the FIS and the ANN; however, the FIS might be considered better since the rule base can be improved on the basis of new observations.

Determining Rail Track Axis Geometry Using Satellite and Terrestrial Geodetic Data

In this paper, a multi-sensor data acquisition system and a method for extracting the track axis geometry of railways are introduced. The rail track surveying system employs GPS and a tracking total station for absolute referencing, aided by a biaxial inclinometer for reducing the data into a common datum in canted sections and a digital odometer for linear referencing. Track axis extraction depends on rail track positional data, whereas its geometric modelling is performed on the basis of design elements using the bearing diagram and the Generalised Least Squares (GLS) method on the horizontal and vertical track layout. In addition, a supplementary algorithm for computing the rail curvature is presented. Extended test runs were conducted to validate the feasibility of the system and the method. The test results show that a directional uncertainty of better than 0.01° in tangent sections and deviations between the actual and computed track radii of better than 1% in curved sections are achievable.

Nonactive antenna compensation for fixed-array microwave imaging--Part I: Model development.

Fixed-array microwave imaging with multisensor data acquisition can suffer from nonactive antenna element interactions which cause distortions in the measurements. In Part I of a two-part paper, we develop a nonactive antenna compensation model for incorporation in model-based near-field microwave image reconstruction methods. The model treats the nonactive members of the antenna array as impedance boundary conditions applied over a cylindrical surface of finite radius providing two parameters, the effective antenna radius and impedance factor, which can be determined empirically from measured data. Results show that the effective radius and impedance factor provide improved fits to experimental data in homogeneous phantoms where measurements are obtained with and without the presence of the nonactive antenna elements. Once deduced, these parameters are incorporated into the nonactive antenna compensation model and lead to systematic data-model match improvements in heterogeneous phantoms. While the improvements afforded by the nonactive antenna model are small on a per measurement basis, they are not insignificant. As shown in Part II, inclusion of this new model for nonactive antenna compensation produces significantly higher quality image reconstructions from measurements obtained with a fixed-array data acquisition system over the frequency band 500-900 MHz.

MKS: A multisensor kernel system

The MKS is presented as a means for multisensor integration and data acquisition. The system has been developed in the context of a robot workstation equipped with various types of sensors utilizing three-dimensional laser range finder data and two-dimensional camera data. Specific goals that have been achieved include: developing a suitable low-level representation on raw data and/or features extracted from the raw data of the various sensors; providing a method for efficient reconfiguration of the sensor system in terms of `logical' sensors which map into physical sensors and computation; and providing a basis for high-level object modeling techniques.