3D Sensor Research Articles

Grasp Pattern Recognition Using Surface Electromyography Signals and Bayesian-Optimized Support Vector Machines for Low-Cost Hand Prostheses

Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial hands. However, these commercially available prosthetic hands are prohibitively expensive for most users. In recent years, advancements in 3D printing and sensor technologies have enabled the design and production of low-cost, externally powered prostheses. This paper presents a pattern-recognition-based human–prosthesis interface that utilizes surface electromyography (sEMG) signals, captured by an affordable device, the Myo armband. A Support Vector Machine (SVM) algorithm, optimized using Bayesian techniques, is trained to classify the user’s intended grasp from among nine common grasping postures essential for daily life activities and functional prosthetic performance. The proposal is viable for real-time implementations on low-cost platforms with 85% accuracy in grasping posture recognition.

3D active-matrix multimodal sensor arrays for independent detection of pressure and temperature.

Pressure and temperature sensing simultaneously and independently is crucial for creating electronic skin that replicates complex sensory functions of human skin. Thin-film transistor (TFT) arrays with sensors have enabled cross-talk-free spatial sensing. However, the thermal dependence of charge transport in semiconductors has resulted in interference between thermal and pressure stimuli. We develop multimodal sensor arrays based on three-dimensional integration of an active matrix to detect temperature and pressure independently. Our approach includes a calibrated compensation to decouple temperature and pressure signals. An individual pixel device consists of a TFT-based pressure sensor layered above a TFT-based temperature sensor. The detected temperature is used to compensate for the thermal effect on TFT-based pressure sensors. We develop large-area sensor arrays to enable accurate detection of two-dimensional pressure and temperature, leveraging these technologies to demonstrate advanced robotic grippers. The grippers stably grasp and lift a cup regardless of temperature, proving their possibility in skin-like electronic applications.

Multifunctional, UV Radiation Shielding and High Bacteriostatic Efficiency 3D Wearable Sensor Triggered by ZIF-67.

3D multifunctional wearable piezoresistive sensors have aroused extensive attention in the fields of motion detection, human-computer interaction, electronic skin, etc. However, current research mainly focuses on improving the foundational performance of piezoresistive sensors, while many advanced demands are often ignored. Herein, a 3D piezoresistive sensor based on rGO@C-ZIF-67@PU is fabricated via high temperature carbonization and a solvothermal reduction method. The as-prepared sensor exhibits a high sensitivity of 245.4 kPa-1, a wide detection range (0-25 kPa), a fast response/recovery time (30 ms/50 ms), and excellent durability (over 5000 cycles). Apart from the outstanding sensing performance, this sensor also displays UV shielding properties and excellent antibacterial activity, providing a broader application prospect for this hybrid sensor. Besides, the optic nerve damage/loss can be compensated to some extent via combining this multifunctional 3D piezoresistive sensor with traditional guide sticks, thus assisting visually impaired people to integrate into social life and normal social interaction easily. Evidently, these outstanding features pave a promising avenue to overcome the limitation of the existing piezoresistive sensors and provide a general way to promote its broad commercial applications.

A shape-reconfigurable electronic composite for stimulus customizable detection via neutral plane shifting.

Three-dimensional (3D) sensors selectively measure the applied force in a particular direction through the designed shape. However, such a fixed sensor design incurs a relatively low sensitivity and narrow measurement range to forces applied from other directions. Here, we report a shape-reconfigurable electronic composite based on a stiffness-tunable polymer and a crack-based strain sensor. The stiffness-tunable polymer allows a high degree of freedom (DOF) in modifying the shape of the electronic composite in its flexible state, enabling the formation of various 3D structures. This modification involves shifting the neutral plane toward the electrode to prevent fractures in the embedded sensors. After modifying the shape of the soft and flexible electronic composite, the dramatically increased stiffness of the stiffness-tunable polymer enables the maintenance of the reconfigured shape of the electronic composite and amplifies the mechanical signal from the external force of the targeted direction by returning the neutral plane to the original position. We validated the reversible modification of the shape of the electronic composite by demonstrating the increase in sensitivity and measured range for targeted external forces (bending, pressing, and stretching) via sequential changes in the designed shapes (wire, spiral, and spring) compared to the initial shape. This facile approach for shape modification will provide an opportunity to realize versatile shape changes in rigid electronics for user purpose.

An Enhanced 3D Sensor Deployment Method for Intelligent Cooperative Sensing in Connected and Autonomous Vehicles

An Enhanced 3D Sensor Deployment Method for Intelligent Cooperative Sensing in Connected and Autonomous Vehicles

Multimodal data visualization method for digital twin campus construction

ABSTRACT University campuses, as distinctive yet commonplace urban environments, face similar challenges of high population density and efficient management. Integrating digital twins into smart campus construction is essential for optimizing campus management. Multimodal data integration and visualization are key to building a digital twin campus. Traditional visualization methods often lack effective inter-data relationships, limiting their capacity to support robust multimodal data integration and create a multidimensional, real-time, comprehensive management view. Therefore, this paper proposes a tailored approach for multimodal data integration and visualization in digital twin campus construction. First, a comprehensive analysis of multimodal campus data identifies spatiotemporal associations among datasets. Subsequently, visualization strategies are developed based on these data characteristics and associations, enabling the multidimensional integration of 3D models, video surveillance, and wireless sensor data. Finally, a prototype system is implemented and evaluated through a case study. The results demonstrate that the proposed method efficiently integrates 2D maps, surveillance videos, and sensor data to create a dynamic, interactive, and panoramic campus view, achieving rendering efficiency exceeding 60 frames per second. Moreover, this adaptable framework can be applied to various campus or geographic contexts, offering critical technical support for multimodal visualization in IoT and big data environments.

A Registration Method Based on Ordered Point Clouds for Key Components of Trains.

Point cloud registration is pivotal across various applications, yet traditional methods rely on unordered point clouds, leading to significant challenges in terms of computational complexity and feature richness. These methods often use k-nearest neighbors (KNN) or neighborhood ball queries to access local neighborhood information, which is not only computationally intensive but also confines the analysis within the object's boundary, making it difficult to determine if points are precisely on the boundary using local features alone. This indicates a lack of sufficient local feature richness. In this paper, we propose a novel registration strategy utilizing ordered point clouds, which are now obtainable through advanced depth cameras, 3D sensors, and structured light-based 3D reconstruction. Our approach eliminates the need for computationally expensive KNN queries by leveraging the inherent ordering of points, significantly reducing processing time; extracts local features by utilizing 2D coordinates, providing richer features compared to traditional methods, which are constrained by object boundaries; compares feature similarity between two point clouds without keypoint extraction, enhancing efficiency and accuracy; and integrates image feature-matching techniques, leveraging the coordinate correspondence between 2D images and 3D-ordered point clouds. Experiments on both synthetic and real-world datasets, including indoor and industrial environments, demonstrate that our algorithm achieves an optimal balance between registration accuracy and efficiency, with registration times consistently under one second.

Functional metallic circuitries created by laser-activated selective electroless plating for 3D customized electronics

Laser-activated selective electroless plating (LASELP) is a promising complementary manufacturing process employed in hybrid additive manufacturing (HAM) technology for the fabrication of customized 3D electronics. However, to the best knowledge of the authors, most current LASELP technologies could only enable copper deposition on/within the polymer matrix, which largely limited the application scope of this technology. Accordingly, an advanced LASELP technology combining catalyst exchanging process is proposed to pattern diverse functional metal on the photopolymer to fabricate 3D electronics. Two kinds of catalyst systems are selected in this HAM technology: (1) Cu2(OH)PO4; (2) antimony tin oxide (ATO) and titania (TiO2). Silver and nickel-phosphorus (Ni-P) alloy were selected as the representatives of direct- and indirect-ELP metals. Silver could directly plated on the laser-activated surface to deposit a dense and highly conductive layer, while for the Ni-P layer, an inevitable catalyst exchange step was applied here to induce Pd0 plating seeds on the laser-activated substrate. Finally, a variety of customized electronics, such as conformal circuit boards, smart structure with strain sensor, embedded structural thermometer, Internet of Things bottle cap, and gas tube integrated with 3D conformal NO2 sensor were implemented are fabricated and fully verify this HAM technology.

Analysis of the measurement accuracy of a thermal 3D sensor for transparent objects

Analysis of the measurement accuracy of a thermal 3D sensor for transparent objects

Indoor space segmentation method based on the gated attention unit

Abstract Accurate 3D scene understanding is a challenging task. With the rapid development of 3D sensor technology, the acquisition and processing of 3D data determine the improvement of scene understanding capabilities. Our paper studies a point cloud semantic segmentation technology based on the Gated Attention Unit (GAU), which integrates the self-attention mechanism and attention pooling through the gated unit structure, thereby obtaining feature representation with long-range dependency information and improving the semantic segmentation effect. Finally, our network achieved 64.6% mIoU and 88.7% OA on S3DIS.

A new enhanced TDEEC protocol for 3D HWSN

Wireless sensor networks (WSNs) are classified as three-dimensional (3D) when the elevation variation of deployed sensor nodes is significant relative to the length and breadth of the operating area. This paper introduces and evaluates a novel protocol, Enhanced Three-Dimensional Efficient TDEEC (ETDEEC-3D), which employs multidimensional scaling to optimize energy consumption in 3D heterogeneous wireless sensor networks (HWSNs) through an improved clustering approach. By excluding nodes closest to the base station from the clustering process, the protocol significantly extends the network lifetime. Performance evaluations indicate that ETDEEC-3D outperforms the TDEEC-3D algorithm in terms of packet delivery, energy efficiency, and the number of cluster heads (CH) formed per round. MATLAB simulations reveal a 24 % improvement in the First Node Dies (FND) metric, a 33 % reduction in energy consumption, and enhanced energy efficiency, achieved by strategically selecting CHs based on nodes' residual energy levels.

Calibration of multi-robot coordinates for collaborative wire arc additive manufacturing using cross-source 3D point cloud models

To decrease the deposition deficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.

Visual Edge Feature Detection and Guidance under 3D Interference: A Case Study on Deep Groove Edge Features for Manufacturing Robots with 3D Vision Sensors

For manufacturing robots equipped with 3D vision sensors, the presence of environmental interference significantly impedes the precision of edge extraction. Existing edge feature extraction methods often enhance adaptability to interference at the expense of final extraction precision. This paper introduces a novel 3D visual edge detection method that ensures greater precision while maintaining adaptability, capable of addressing various forms of interference in real manufacturing scenarios. To address the challenge, data-driven and traditional visual approaches are integrated. Deep groove edge feature extraction and guidance tasks are used as a case study. R-CNN and improved OTSU algorithm with adaptive threshold are combined to identify groove features. Subsequently, a scale adaptive average slope sliding window algorithm is devised to extract groove edge points, along with a corresponding continuity evaluation algorithm. Real data is used to validate the performance of the proposed method. The experiment results show that the average error in processing interfered data is 0.29mm, with an average maximum error of 0.54mm, exhibiting superior overall performance and precision compared to traditional and data-driven methods.

Characterisation of 3D trench silicon pixel sensors irradiated at 1⋅1017 1 MeV neqcm-2

The 3D trench silicon pixel sensors developed by the TimeSPOT collaboration have demonstrated exceptional performance, even after exposure to extreme radiation fluences up to 1⋅1017 1 MeV neq/cm2. This study assesses the radiation tolerance of these sensors using minimum ionizing particles during a beam test campaign. The results indicate that while radiation damage reduces charge collection efficiency and overall detection efficiency, these losses can be mitigated to levels comparable to non-irradiated sensors by increasing the reverse bias voltage. Charge multiplication was observed and characterised for the first time in 3D trench sensors, revealing a distinct operating regime post-irradiation achievable at bias voltages close to 300 V. Additionally, the timing performance of irradiated sensors remains comparable to their non-irradiated counterparts, underscoring their resilience to radiation damage. Currently, 3D trench silicon detectors are among the fastest and most radiation-hard pixel sensors available for vertex detectors in high-energy physics colliders. These findings highlight the potential of these sensors for new 4D tracking systems of future experiments at the Future Circular Hadron Collider (FCC-hh), advancing the capabilities of radiation-hard sensor technology.

Phononic crystal-based pH sensing and its classification with machine learning

We present a 3D phononic crystal-based pH sensor made of a biocompatible hydrogel-SiO2 composite, its classification via machine learning (ML), and theoretical and experimental evidence for the existence of a band gap (BG). Instead of spatial, spectral approach is considered to classify different pH levels. The transition in band diagram and transmission coefficient measured along [111] direction reveals that the composite is sensitive to pH variation. Dips showing the BG in transmission spectra shift towards lower frequencies as the pH value increases. In addition, we have demonstrated the potential of four ML algorithms: k-nearest neighbor (KNN), Naïve Bayes, Support vector machine (SVM) and Neural Networks (NNs) as a quick identifying tool for the expedited and accurate classification of pH values with sensitivity S=0.3508 MHz/pH up to resolution of R= 0.1 pH level change with only 53 observations. For boosting the predicting power of model, we apply data augmentation technique on dataset. Our work focuses on multiclass supervised ML techniques which have the ability to classify pH values between 4 and 7 in 31 labels. In this comparative study we have explored the capability of each algorithm based upon the performance metrics. In addition, we found that except Naïve Bayes all algorithms could predict with >99 % accuracy. Lastly, globally optimal hyperparameters for NNs are also presented. The proposed pH sensor and classification method has pronounced potential to be used for in-vivo powerless pH measurements.

Electromagnetic responses on microstructures of duplex stainless steels based on 3D cellular and electromagnetic sensor finite element models

Electromagnetic responses on microstructures of duplex stainless steels based on 3D cellular and electromagnetic sensor finite element models

BIOMECHANICAL ASSESSMENT OF SCAPULAR PLANE ELEVATION EXERCISE WITH DIFFERENT MODES OF RESISTANCE IN SUBJECTS WITH AND WITHOUT IMPINGEMENT SYNDROME.

Background: Strengthening the rotator cuff and scapular stabilizers is usually prescribed for subjects with shoulder pain and is done either using dumbbells or TheraBand. The resistance offered by elastic Theraband increases with increase in change from initial length and therefore the resistance offered by TheraBand, and dumbbell differs across the range of motion. Objectives: The purpose of the study is to evaluate the scapular kinematics and activity of prime mover (Anterior Deltoid) while using two different modes of resistance (TheraBand v/s dumbbell) in subjects with and without impingement syndrome. Methods: Twenty-two asymptomatic subjects and 12 subjects with impingement syndrome were included. 3D motion sensors were attached over the humerus, scapulae and trunk to capture movements of the shoulders as the subject performed scapular elevation without load, with yellow Theraband or 2 Kg Dumbbell. Simultaneous electromyography (EMG) activity of anterior Deltoid was measured and analyzed as percentage of maximum voluntary contraction. Results: During arm elevation, scapular Upward rotation was significantly greater at 90° with TheraBand® as compared to unloaded. It was also greater at 120° as compared to unloaded and dumbbell conditions (p<0.001) during elevation and lowering phase. Scapula upward rotation was significantly lesser at 30° with TheraBand® as compared to other conditions. Conclusion: This study found significant downward rotation and anterior tilt of scapula with TheraBand during lowering phase of exercise as compared to dumbbell and unloaded. The alterations of kinematics may help therapists make informed decisions while making exercise prescriptions.

Associative graph convolution network for point cloud analysis

Since point cloud is the raw output of most 3D sensors, its effective analysis is in huge demand in the field of autonomous driving and robotic manipulation. However, directly processing point clouds is challenging because point clouds are a kind of disordered and unstructured geometric data. Recently, numerous graph convolution neural networks are proposed for introducing graph structure to point clouds yet far from perfect. Specially, DGCNN tries to learn local geometric of points in semantic space and recomputes the graph using nearest neighbors in the feature space in each layer. However, it discards all the information of the previous graph after each graph update, which neglects the relations between each dynamic update. To this end, we propose an associative graph convolution neural network (AGCN) which mainly consists of associative graph convolution (AGConv) and two kinds of residual connections. AGConv additionally considers the information from the previous graph when computing the edge function on current local neighborhoods in each layer, and it can precisely and continuously capture the local geometric features on point clouds. Residual connections further explore the semantic relations between layers for effective learning on point clouds. Extensive experiments on several benchmark datasets show that our network achieves competitive classification and segmentation results.

Enhancing Fall Detection Accuracy: The Ground-Face Coordinate System for 3D Accelerometer Data

The global elderly population is on the rise, leading to increased physical, sensory, and cognitive changes that heighten the risk of falls. Consequently, fall detection (FD) has emerged as a significant concern, attracting considerable attention in recent years. Utilizing 3D accelerometer sensors for FD offers advantages such as cost-effectiveness and ease of implementation; however, traditional raw 3D accelerometer signals are inherently dependent on the device's orientation and placement within the device coordinate system. Misalignment between the device's axes and the direction of movement can lead to misinterpretation of acceleration signals, potentially causing misclassification of activities and resulting in false positives or missed falls. This study introduces a novel coordinate system called "ground-face," which is designed to be independent of the device's orientation and placement. In this system, the vertical axis is aligned perpendicularly to the Earth, while the device's x-axis is aligned with the individual's direction of movement. To assess the potential of the vertical component of ground-face referenced accelerometer signals for FD, it was compared with the commonly used acceleration magnitude signal. Detailed analysis was conducted using frequently preferred features in FD studies, and fall detection was performed with various classifiers. Comprehensive experiments demonstrated that the vertical component of the ground-face signal effectively characterizes falls, yielding approximately a 2% improvement in detection accuracy. Moreover, the proposed coordinate system is not limited to FD but can also be applied to human activity recognition (HAR) systems. By mitigating orientation-related discrepancies, it reduces the likelihood of misclassification and enhances the overall HAR capabilities.

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.