Real-time Motion Research Articles

Bioinspired Conductivity-Enhanced, Self-Healing, and Renewable Silk Fibroin Hydrogel for Wearable Sensors with High Sensitivity.

The development of silk fibroin-based hydrogels with excellent biocompatibility, aqueous processability, and facile controllability in structure is indeed an exciting advancement for biological research and strain sensor applications. However, silk fibroin-based hydrogel strain sensors that combine high conductivity, high stretchability, reusability, and high selectivity are still desired. Herein, we report a simple method for preparing double-network hydrogels including silk fibroin and poly(acrylic acid) sodium-polyacrylate (PAA-PAAS) networks. The conformation and aggregate of silk fibroin could be facilely tuned by both ions and pH resulting from the PAA-PAAS network. The optimized hydrogel exhibits intriguing properties, such as high conductivity (3.67 S/m) and transparency, high stretchability (1186%) with a tensile strength of 110 kPa, good adhesion properties, reversible compression, self-healing, and high sensitivity (GF = 10.71). This hydrogel strain sensor can detect large-scale and small human movements in real time, such as limb movements, heartbeats, and pulse. Additionally, its ability to adsorb water and recover effectiveness after losing water from air with 90% humidity along with the capability for low-temperature motion detection facilitated by ethylene glycol further enhance its practical utility. This work offers a novel and simple approach to design flexible bionic strain sensors.

Hand Gesture Recognition for Virtual Mouse Control

Our work delved into the complexities of real-time hand motion interpretation and fingertip recognition to simulate the functionality of a traditional mouse. We developed a python-based technique that seamlessly translates hand movements into mouse commands by analyzing the angles between fingers and calculating the ratio of the hand’s silhouette to its convex hull. Our methodology was refined to ensure an intuitive and accurate user experience. However, challenges remained in achieving robustness and accuracy in gesture recognition systems in various scenarios, including variations in lighting, hand orientation, and individual human characteristics. These factors had a significant impact on system performance and reliability. To address these challenges, our approach incorporated the algorithms and machine learning models designed to adapt to different conditions. Despite these advances, further research and development were essential to improve the reliability and comprehensiveness of gesture recognition technologies.

A real-time tumor position prediction based multi-dimensional respiratory motion compensation puncture method

Objective.This study proposes a real-time tumor position prediction-based multi-dimensional respiratory motion compensation puncture method to accurately track real-time lung tumors and achieve precise needle puncture.Approach.A hybrid model framework integrating prediction and correlation models is developed to enable real-time tumor localization. A Long Short-Term Memory neural network with bidirectional and attention modules (Bi-LSTM-ATT) is employed for predicting external respiratory signals. Subsequently, a backpropagation neural network is constructed to correlate these signals with tumor positions. Tumor trajectory decomposition and the determination of an optimal puncture window based on multiple criteria ensure accurate needle puncture.Main results.When the delay time of Bi-LSTM-ATT model is 500 ms, its RMSE, MAE, andR2are 0.0482 mm, 0.0414 mm, and 97.90% respectively. The correlation model locates lung tumors in 10 cases with a target registration error within 0.74 mm. The proposed puncture method achieves a puncture error ranging from 1.00 mm to 1.32 mm, with an average error of 1.2 mm.Significance.The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for percutaneous biopsy procedures within the lung.Clinical trial registrationClinical trial registration was not required for this research.

A SAC-Bi-RRT Two-Layer Real-Time Motion Planning Approach for Robot Assembly Tasks in Unstructured Environments

Due to the uncertainty and complexity of the assembly process, the trajectory planning of a robot needs to consider the real-time obstacle avoidance problem when it completes the assembly in the unstructured workspace. To realize the safe assembly of assembly robots in dynamic and complex environments, a dynamic obstacle avoidance trajectory planning method for robots combining traditional planning algorithms and deep reinforcement learning algorithms is proposed to improve the robot’s agent and obstacle avoidance ability in dynamic and complex environments. The Bidirectional Rapidly-exploring Random Tree (Bi-RRT) method is utilized as a global planner to plan the global optimal path quickly; considering the real-time nature of the assembly process, the Soft Actor-Critic (SAC) is used as a local obstacle avoider to avoid obstacles more accurately and to find the nearest node generated by the Bi-RRT during the planning process, which is regarded as the goal during the local obstacle avoidance to reduce the model’s complexity. By training and testing in the simulation engine and comparing with SAC, DDPG and DQN algorithms, the method can avoid obstacles in dynamic and complex environments more efficiently, which verifies that the proposed hybrid method can accomplish the high-precision planning task with a high success rate.

Full Body-Worn Textile-Integrated Nanomaterials and Soft Electronics for Real-Time Continuous Motion Recognition Using Cloud Computing.

Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system's performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.

Ultraprecision multi-axis CARIC control strategy with application to a nano-accuracy air-bearing motion stage.

Multi-axis contouring control is crucial for ultraprecision manufacturing industries, contributing to meeting the ever-increasingly stringent performance requirements. In this article, a novel contouring adaptive real-time iterative compensation (CARIC) method is proposed to achieve extreme multi-axis contouring accuracy, remarkable trajectory generalization, disturbance rejection, and parametric adaptation simultaneously. Specifically, control actions generated by CARIC consist of robust feedback, adaptive feedforward, and online trajectory compensation components. Robust feedback and adaptive feedforward terms initially stabilize single-axis closed-loop control systems and adapt to parameter variations. An online contouring error prediction model subsequently captures upcoming contouring errors in advance, enabling the iterative calculation of optimal online trajectory compensation signals at each sampling instant during real-time motion. This mechanism proactively suppresses potential contouring errors before their occurrence. Comparative simulations and experiments demonstrate that the proposed CARIC method reaches the accuracy limit previously attainable only by iterative learning control (ILC) while enhancing trajectory generalization, disturbance rejection, and parametric adaptation. Notably, practical experiments on a nano-accuracy air-bearing motion stage showcase consistent 7-nm-level accuracy across various 100-mm stroke contouring tasks even under varying contours, payloads, and disturbances. Owing to these advantages, CARIC offers promising potential to enhance ultraprecision manufacturing performance through advanced motion control techniques.

Real-time CBCT imaging and motion tracking via a single arbitrarily-angled x-ray projection by a joint dynamic reconstruction and motion estimation (DREME) framework

Objective.Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkdynamicreconstruction andmotionestimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.Approach.DREME incorporates a deep learning-based real-time CBCT imaging and motion estimation method into a dynamic CBCT reconstruction framework. The reconstruction framework reconstructs a dynamic sequence of CBCTs in a data-driven manner from a standard pre-treatment scan, without requiring patient-specific prior knowledge. Meanwhile, a convolutional neural network-based motion encoder is jointly trained during the reconstruction to learn motion-related features relevant for real-time motion estimation, based on a single arbitrarily-angled x-ray projection. DREME was tested on digital phantom simulations and real patient studies.Main Results.DREME accurately solved 3D respiration-induced anatomical motion in real time (∼1.5 ms inference time for each x-ray projection). For the digital phantom studies, it achieved an average lung tumor center-of-mass localization error of 1.2 ± 0.9 mm (Mean ± SD). For the patient studies, it achieved a real-time tumor localization accuracy of 1.6 ± 1.6 mm in the projection domain.Significance.DREME achieves CBCT and volumetric motion estimation in real time from a single x-ray projection at arbitrary angles, paving the way for future clinical applications in intra-fractional motion management. In addition, it can be used for dose tracking and treatment assessment, when combined with real-time dose calculation.

Event-Based Visual Simultaneous Localization and Mapping (EVSLAM) Techniques: State of the Art and Future Directions

Recent advances in event-based cameras have led to significant developments in robotics, particularly in visual simultaneous localization and mapping (VSLAM) applications. This technique enables real-time camera motion estimation and simultaneous environment mapping using visual sensors on mobile platforms. Event cameras offer several distinct advantages over frame-based cameras, including a high dynamic range, high temporal resolution, low power consumption, and low latency. These attributes make event cameras highly suitable for addressing performance issues in challenging scenarios such as high-speed motion and environments with high-range illumination. This review paper delves into event-based VSLAM (EVSLAM) algorithms, leveraging the advantages inherent in event streams for localization and mapping endeavors. The exposition commences by explaining the operational principles of event cameras, providing insights into the diverse event representations applied in event data preprocessing. A crucial facet of this survey is the systematic categorization of EVSLAM research into three key parts: event preprocessing, event tracking, and sensor fusion algorithms in EVSLAM. Each category undergoes meticulous examination, offering practical insights and guidance for comprehending each approach. Moreover, we thoroughly assess state-of-the-art (SOTA) methods, emphasizing conducting the evaluation on a specific dataset for enhanced comparability. This evaluation sheds light on current challenges and outlines promising avenues for future research, emphasizing the persisting obstacles and potential advancements in this dynamically evolving domain.

IMU Sensor-Based Worker Behavior Recognition and Construction of a Cyber-Physical System Environment.

According to South Korea's Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers' angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer's gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers' safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber-physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors.

Optimal Planning Target Volume Margins to Account for Intra-Fractional Prostate Motion Relative to Treatment Duration: A Study Using Real-Time Transperineal Ultrasound Guidance.

Prostate motion during external beam radiotherapy (EBRT) is common and typically managed using fiducial markers and cone beam CT (CBCT) scans for inter-fractional motion correction. However, real-time intra-fractional motion management is less commonly implemented. This study evaluated the extent of intra-fractional prostate motion using transperineal ultrasound (TPUS) and examined the impact of treatment time on prostate motion. Patients undergoing prostate EBRT with TPUS at a single institution from August 2016 to August 2021 were analysed. Pre-treatment daily CBCT corrected inter-fractional prostate shift. Continuous intra-fractional prostate motion was recorded at two frames per second in three dimensions, with three-dimensional (3D) displacement calculated as a vector. Motion data were modelled to determine the probability of the prostate remaining within pre-specified PTV margins relative to treatment delivery time. The study analysed 3364 fractions delivered to 122 patients. The mean treatment delivery time was 3.8 min. The prostate remained within a 5 mm margin with high frequencies in the superior-inferior (SI) and left-right (LR) directions, 97.8% and 98.4% of fractions respectively while 5.5% of fractions had deviations greater than 5 mm in the anterior-posterior (AP) direction. By contrast, the 3D vector exceeded a 5 mm margin in 14.5% of fractions. Drift motion modelling indicated a 99% probability of the vector staying within a 3 mm margin for 2 min, while for a 5 mm margin, the duration extended to 3.4 min. Intra-fractional prostate motion monitoring is increasingly important as SABR with reduced PTV margins are utilised in prostate radiotherapy. Smaller PTV margins and longer treatment time require real-time monitoring to avoid geographical miss.

Multi-Sensor Smart Glove With Unsupervised Learning Model for Real-Time Wrist Motion Analysis in Golf Swing Biomechanics

Multi-Sensor Smart Glove With Unsupervised Learning Model for Real-Time Wrist Motion Analysis in Golf Swing Biomechanics

Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment.

Real-time moving target trajectory prediction is highly valuable in applications such as automatic driving, target tracking, and motion prediction. This paper examines the projection of three-dimensional random motion of an object in space onto a sensing plane as an illustrative example. Historical running trajectory data are used to train a reserve network. The trained network model is subsequently used to predict future trajectories. In the experiment, a network model trained on 20 000 frames of random running trajectory data was used to predict trajectories for 1-20 future frames, and 5000 frames were used for testing. The results showed prediction errors for 80% of the predictions of less than 0.01%, 0.8%, and 4% for 1, 10, and 20 future frames, respectively.

Application and optimization of FPGA-based real-time motion control systems in robotics

Application and optimization of FPGA-based real-time motion control systems in robotics

Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.

Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation. EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning. In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants. Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.

Application of human-computer interaction technology integrating biomimetic vision system in animation design with a biomechanical perspective

The combination of Human-Computer Interaction (HCI) technology with biomimetic vision systems has transformational potential in animation design, particularly by incorporating biomechanical principles to create immersive and interactive experiences. Traditional animation approaches frequently lack sensitivity to real-time human motions, which can restrict engagement and realism. This study addresses this constraint by creating a framework that uses Virtual Reality (VR) and Augmented Reality (AR) to generate dynamic settings that include a variety of human activities, informed by biomechanical analysis. A biomimetic vision system is used to record these motions with wearable sensors, allowing for precise monitoring of user activity while considering biomechanical factors such as joint angles, force distribution, and movement patterns. The recorded data is preprocessed using Z-score normalization methods and extracted using Principal Component Analysis (PCA). This study proposed an Egyptian Vulture optimized Adjustable Long Short-Term Memory Network (EVO-ALSTM) technique for motion classification, specifically tailored to recognize biomechanical characteristics of human movements. Results demonstrate a significant improvement in precision (93%), F1-score (91%), accuracy (95%), and recall (90%) for the motion recognition system, highlighting the effectiveness of biomechanical insights in enhancing animation design. The findings indicate that integrating real-time biomechanical data into the animation process leads to more engaging and realistic user experiences. This study not only advances the subject of HCI but also provides the framework for future investigations into sophisticated animation technologies that use biomimetic and biomechanical systems.

Dynamic Digital Radiography (DDR) in the Diagnosis of a Diaphragm Dysfunction.

Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator's experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm's motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm.

Effect of Different Pressures on Polymer Flow at a Contraction Path: A Real-Time Numerical Approach

The complexity of polymer flow through a contraction arises from the simultaneous occurrence of shear and elongational strains near the entrance of the contraction path (die). Although this phenomenon has been extensively studied, the effect of plunger motion on the flow toward the contraction path remains underexplored. This study investigated the rheological behavior of thermoplastic polymers at a contraction flow path to enhance the understanding of their flow and rheological behavior, including variations in velocity, pressure, viscosity, and shear rate under varying loads. Polypropylene (PP), a semicrystalline polymer with a low melting point, was used as the test material. The operating temperature was set to 180 °C, and the displacement of the plunger, marked with black lines at its initial and final heights, was recorded under loads of 0.3, 0.6 and 0.85 MPa. The displacement rates were analyzed using MATLAB. A dynamic mesh approach in ANSYS 19 was employed to simulate the real-time motion of the plunger, incorporating a user-defined function developed in C language to control the dynamic boundary motion. The numerical approach successfully simulated the rates of plunger displacement (speed) and predicted the viscosity of PP within the paths (barrel and die). Results indicated that the plunger speed increased with pressure. The pressure generated by the plunger created a driving force that overcame the resistance to flow within the barrel. Higher pressure from the plunger resulted in a greater driving force, which increased the flow rate of the polymer melt through the barrel. On the other side, as the polymer melt flowed from a large cross-sectional area to the contraction flow area, the velocity of the polymer molecules increased, resulting in a pressure drop in the PP melt. Polymer molecules became oriented and stretched in the flow direction upon entering the contraction area, further increasing the shear rate. Consequently, the reduced cross-sectional area in the contraction increased the flow rate, elevated the shear rate, and decreased the viscosity, facilitating polymer flow through the contraction.

Application of IgH EtherCAT Master for Ultra-Precision Motion Control of Precision Axes.

The EtherCAT fieldbus system is widely applied in different types of computerized numerical control (CNC) machine tools due to its outstanding communication performance. In the field of ultra-precision CNC, some machine tools employ controllers that integrate EtherCAT master functionality to achieve real-time communication with other devices; however, the open-source IgH EtherCAT master has rarely been applied to the CNC systems of ultra-precision machine tools. The feasibility of using the IgH EtherCAT master to meet the communication performance requirements of ultra-precision machine tools remains uncertain; therefore, it is necessary to validate the control effect on precision axes under the application of the IgH EtherCAT master. In this work, EtherCAT applications were developed on a personal computer (PC) to alter it to a bus-type controller with the IgH EtherCAT master function. To provide the EtherCAT master with real-time and accurate motion data of the axes, an interpolation algorithm tailored for control experiments was designed, and a G-code data processing method was proposed. Moreover, precision aerostatic linear axes and servo drivers were chosen as EtherCAT slaves for single-axis motion and dual-axis linkage control experiments. The experimental results showed that the motion controller based on IgH can effectively control the precision axes to execute ultra-precision linear and circular interpolation motion.

Trojan Horse Bioheterojunction Empowers Adhesive Hydrogel with Robust Antibacterial Activity and Sensing Capacity for Infected Cutaneous Regeneration.

Strategic integration of adhesive hydrogels with phototherapy-based antibacterial properties has been extensively leveraged in infected tissue repair. Nevertheless, the interference of bacterial heat shock proteins and antioxidant defense systems attenuates the bactericidal potency of phototherapy. To address this imposing predicament, a Trojan horse bioheterojunction (Th-bioHJ) incorporating liquid metal and copper sulfide is devised to confer an adhesive hydrogel with multimodal and comprehensive antibacterial properties for remedying infectious wounds. Th-bioHJ generates phototherapeutic effects and interrupts the electron transport chain with the Trojan horse strategy, achieving rapid and robust sterilization. Additionally, Th-bioHJ promotes cell migration and angiogenesis. In vivo studies elucidate the remarkable efficacy of Th-bioHJ hydrogel in rapidly eradicating bacteria, promoting angiogenesis and boosting infectious cutaneous regeneration. Meanwhile, the Th-bioHJ hydrogel demonstrates exceptional biointerface adhesion and sensing capabilities for real-time motion monitoring. This study provides groundbreaking insights into the innovative application of an adhesive hydrogel in the management of infected wounds.

HoloGaussian Digital Twin: Reconstructing 3D Scenes with Gaussian Splatting for Tabletop Hologram Visualization of Real Environments

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins.