Facile One-Pot Preparation of Polypyrrole-Incorporated Conductive Hydrogels for Human Motion Sensing.

Conductive hydrogels have been widely used in soft robotics, as well as skin-attached and implantable bioelectronic devices. Among the candidates of conductive fillers, conductive polymers have become popular due to their intrinsic conductivity, high biocompatibility, and mechanical flexibility. However, it is still a challenge to construct conductive polymer-incorporated hydrogels with a good performance using a facile method. Herein, we present a simple method for the one-pot preparation of conductive polymer-incorporated hydrogels involving rapid photocuring of the hydrogel template followed by slow in situ polymerization of pyrrole. Due to the use of a milder oxidant, hydrogen peroxide, for polypyrrole synthesis, the photocuring of the hydrogel template and the growing of polypyrrole proceeded in an orderly manner, making it possible to prepare conductive polymer-incorporated hydrogels in one pot. The preparation process is facile and extensible. Moreover, the obtained hydrogels exhibit a series of properties suitable for biomedical strain sensors, including good conductivity (2.49 mS/cm), high stretchability (>200%), and a low Young's modulus (~30 kPa) that is compatible with human skin.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis.

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

Impact of interplay effects on spot scanning proton therapy with motion mitigation techniques for lung cancer: SFUD versus robustly optimized IMPT plans utilizing a four-dimensional dynamic dose simulation tool.

BACKGROUND: The interaction between breathing motion and scanning beams causes interplay effects in spot-scanning proton therapy for lung cancer, resulting in compromised treatment quality. This study investigated the effects and clinical robustness of two types of spot-scanning proton therapy with motion-mitigation techniques for locally advanced non-small cell lung cancer (NSCLC) using a new simulation tool (4DCT-based dose reconstruction). METHODS: Three-field single-field uniform dose (SFUD) and robustly optimized intensity-modulated proton therapy (IMPT) plans combined with gating and re-scanning techniques were created using a VQA treatment planning system for 15 patients with locally advanced NSCLC (70 GyRBE/35 fractions). In addition, gating windows of three or five phases around the end-of-expiration phase and two internal gross tumor volumes (iGTVs) were created, and a re-scanning number of four was used. First, the static dose (SD) was calculated using the end-of-expiration computed tomography (CT) images. The four-dimensional dynamic dose (4DDD) was then calculated using the SD plans, 4D-CT images, and the deformable image registration technique on end-of-expiration CT. The target coverage (V98%, V100%), homogeneity index (HI), and conformation number (CN) for the iGTVs and organ-at-risk (OAR) doses were calculated for the SD and 4DDD groups and statistically compared between the SD, 4DDD, SFUD, and IMPT treatment plans using paired t-test. RESULTS: In the 3- and 5-phase SFUD, statistically significant differences between the SD and 4DDD groups were observed for V100%, HI, and CN. In addition, statistically significant differences were observed for V98%, V100%, and HI in phases 3 and 5 of IMPT. The mean V98% and V100% in both 3-phase plans were within clinical limits (> 95%) when interplay effects were considered; however, V100% decreased to 89.3% and 94.0% for the 5-phase SFUD and IMPT, respectively. Regarding the significant differences in the deterioration rates of the dose volume histogram (DVH) indices, the 3-phase SFUD plans had lower V98% and CN values and higher V100% values than the IMPT plans. In the 5-phase plans, SFUD had higher deterioration rates for V100% and HI than IMPT. CONCLUSIONS: Interplay effects minimally impacted target coverage and OAR doses in SFUD and robustly optimized IMPT with 3-phase gating and re-scanning for locally advanced NSCLC. However, target coverage significantly declined with an increased gating window. Robustly optimized IMPT showed superior resilience to interplay effects, ensuring better target coverage, prescription dose adherence, and homogeneity than SFUD. TRIAL REGISTRATION: None.

Evaluation of the impact of filter types and parameters upon the accuracy of phase-based optical flow method with a complex steerable pyramid.

Complex steerable pyramid (CSP) performs well when applied to magnify subtle motions of structures for observing the dynamic characteristics of facilities. However, the impact of the types and parameters of CSP filters upon the performance of phase-based optical flow (PBOF) in measuring motion parameters has not been systematically studied. The purpose of this study is to comprehensively evaluate the impact of different CSP filter types (Octave, HalfOctave, SmoothHalfOctave, and QuarterOctave) and parameters on the performance of PBOF in measuring motion parameters. Firstly, by measuring simulated translational motion, the influence of the CSP's down-sampling rates on the displacement measurement accuracy of PBOF is analyzed to determine appropriate settings. Subsequently, the effective displacement measurement interval and accuracy of PBOF using the CSP are studied through simulated and experimental translational motion measurements. Further, the vibration parameter's accuracy is analyzed through simulated periodic vibration measurements. Finally, the characteristics of PBOF using the four kinds of CSP and practical considerations are discussed. Simulation and experimental results demonstrate that when using middle-level filters within the effective level range of HalfOctave, PBOF achieves the best overall displacement measurement performance. Additionally, this method can easily integrate with signal processing techniques in analyzing structural dynamic characteristics under field conditions.

[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Influence of ground motion parameters on seismic response of a large-longitudinal-slope and small-radius curved girder bridge.

The mechanical performance of curved bridges under the action of an earthquake is complex. To obtain the influence of seismic parameters on the seismic response of curved girder bridges, this paper relies on a large slope small-radius curved steel box girder bridge (LSCGB) and selects seismic wave incidence angle, vertical component of ground motion, and site category as seismic parameters to carry out nonlinear time history analysis. Based on the analysis results of the case bridge, it is shown that the torsional vibration of the first 10 modes of LSCGB is significant, the modes are dispersed, and the contribution of high-order modes of vibration cannot be ignored. The most unfavorable seismic wave incidence angle is in the direction of 45°âˆ¼60° counterclockwise Angle from the central connection line of Pier No. 1 and Pier No. 4 of the bridge. The seismic response of the curved bridge components increases with the vertical seismic intensity, and the influence on displacement responses is more significant. The basic vibration period of curved girder bridges built on soft soil sites is extended by approximately 18.23%, and the seismic response of key components increases with the softening of the site soil. Therefore, when analyzing the seismic response of LSCGBs, the influence of vertical component of ground motion and site category should not be ignored.

Integrating High-Performance Flexible Wires with Strain Sensors for Wearable Human Motion Detection.

Flexible electronics have revolutionized the field by overcoming the rigid limitations of traditional devices, offering superior flexibility and adaptability. Conductive ink performance is crucial, directly impacting the stability of flexible electronics. While metal filler-based inks exhibit excellent conductivity, they often lack mechanical stability. To address this challenge, we present a novel conductive ink utilizing a ternary composite filler system: liquid metal and two micron-sized silver morphologies (particles and flakes). We systematically investigated the influence of filler type, mass ratio, and sintering process parameters on the composite ink's conductivity and mechanical stability. Our results demonstrate that flexible wires fabricated with the liquid metal/micron silver particle/micron silver flake composite filler exhibit remarkable conductivity and exceptional bending stability. Interestingly, increasing the liquid metal content results in a trade-off, compromising conductivity while enhancing mechanical performance. After enduring 5000 bending cycles, the resistance change in wires formulated with a 4:1 mass ratio of micron silver particles to flakes is only half that of wires with a 1:1 ratio. This study further investigates the mechanism governing resistance variations during flexible wire bending. Additionally, we observed a positive correlation between sintering temperature and pressure with the conductivity of flexible wires. The significance of the sintering parameters on conductivity follows a descending order: sintering temperature, sintering pressure, and sintering time. Finally, we demonstrate the practical application of this technology by integrating the composite ink-based flexible wires with conductive polymer-based strain sensors. This combination successfully achieved the detection of human movements, including finger and wrist bending.

Effect of simultaneous multislice imaging, slice properties, and repetition time on the measured magnetic resonance biexponential intravoxel incoherent motion in the liver.

OBJECTIVES: This study aims to investigate the previously reported dependency of intravoxel incoherent motion (IVIM) parameters on simultaneous multislice (SMS) acquisition and repetition time (TR). This includes the influence of slice thickness, slice gaps, and slice order on measured IVIM parameters. MATERIALS AND METHODS: Diffusion-weighted imaging (DWI) of the liver was performed on 10 healthy volunteers (aged 20-30 years) at 3T with a slice thickness of 5 mm, a slice gap of 5 mm, and a linear slice order. Diffusion-weighted images were acquired with 19 b-values (0-800 s/mm2) using both conventional slice excitation with an acceleration factor of one (AF1) and SMS excitation with an acceleration factor of three (AF3). Each of these measurements were carried out with two repetition times (TRs)- 1,300 ms (prefix s) and 4,500 ms (prefix l)-resulting in four different combinations: sAF1, sAF3, lAF1, and lAF3. Five volunteers underwent additional measurements using a 10 mm slice thickness and with AF1. Median signal values in the liver were used to determine the biexponential IVIM parameters. Statistical significances were assessed using the Kruskal-Wallis test, Wilcoxon signed-rank test, and Student's t-test. In-silico investigations were also used to interpret the data. RESULTS: There were no significant differences between the biexponential IVIM parameters acquired from sAF1, sAF3, lAF1, and lAF3. Median values of the perfusion fraction f were as follows: 29.9% (sAF1), 26.9% (sAF3), 28.1% (lAF1), and 27.5% (lAF3). In the 10 mm-thick slices, f decreased from 31.3% (lAF1) to 27.4% (sAF1) (p = 0.141). CONCLUSION: The slice excitation mode did not appear to have any significant influence on the biexponential IVIM parameters. However, our simulations, as well as values reported from previous publications, show that slice thickness, slice gaps, and slice order are relevant and should thus be reported in IVIM studies.

A Parallel Robot With Remote Centre-of-Motion for Eye Surgery: Design, Kinematics, Prototype, and Experiments.

BACKGROUND: Millions of patients suffering from eye disease cannot receive proper treatment due to the lack of qualified surgeons. Medical robots have the potential to solve this problem and have attracted significant attention in the research community. METHOD: This paper proposes a novel parallel robot with a remote centre of motion for minimally invasive eye surgery. Kinematics models, singularity and workspace analyses, and dimension optimisation are conducted. A prototype was developed, and experiments were conducted to test its mobility, accuracy, precision and stiffness. RESULTS: The prototype robot can successfully perform the required motions, and has a precision ranging from 7 ± 2 µm to 30 ± 8 µm, accuracy from 21 ± 10 µm to 568 ± 374 µm, and stiffness ranging from 1.22 ± 0.39 N/mm to 10.53 ± 5.18 N/mm. CONCLUSION: The prototype robot has a great potential for performing the minimally invasive surgery. Its stiffness meets the design requirement, but its accuracy and precision need to be further improved.

Optimizing motion detection performance: Harnessing the power of squeeze and excitation modules.

This paper introduces an innovative segmentation model that extends the U-Net architecture with a Squeeze and Excitation (SE) mechanism, designed to enhance the detection of moving objects in video streams. By integrating this model into the ViBe motion detection algorithm, we have significantly improved detection accuracy and reduced false positive rates. Our approach leverages adaptive techniques to increase the robustness of the segmentation model in complex scenarios, without requiring extensive manual parameter tuning. Despite the notable improvements, we recognize that further training is necessary to optimize the model for specific applications. The results indicate that our method provides a promising direction for real-time motion detection systems that require high precision and adaptability to varying conditions.

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

Design and Motion Control of Master-Slave Control Endotracheal Intubation Robot.

BACKGROUND: Master-slave remote control technology allows patients to be treated promptly during transport and also reduces the risk of contagious infections. Endotracheal intubation, guided by endoscopy and a master-slave system, enables doctors to perform the procedure efficiently and accurately. METHODS: In this paper, we present the development of a master-slave controlled endotracheal intubation robot (EIR). It is based on operation incremental mapping, a weighted recursive average filtering method to reduce vibration, and a virtual fixture designed to reduce mishandling in minimally invasive surgery. RESULTS: Simulation analysis of the master-slave control demonstrates that the weighted recursive average filtering method effectively reduces vibration, while the virtual fixture assists in confining the operator's movement within a delimited area. Experimental validation confirms the validity of the robot's structural design and control method. CONCLUSIONS: The developed robot successfully achieves the necessary motion for endotracheal intubation surgery through master-slave control.

Programming Fluid Motion Using Multi-Enzyme Micropump Systems.

In the presence of appropriate substrates, surface-anchored enzymes can act as pumps and propel fluid through microchambers. Understanding the dynamic interplay between catalytic reactions and fluid flow is vital to enhancing the accuracy and utility of flow technology. Through a combination of experimental observations and numerical modeling, we show that coupled enzyme pumps can exhibit flow enhancement, flow suppression, and changes in the directionality (reversal) of the fluid motion. The pumps' ability to regulate the flow path is due to the reaction selectivity of the enzymes; the resultant fluid motion is only triggered by the presence of certain reactants. Hence, the reactants and the sequence in which they are present in the solution and the layout of the enzyme-attached patches form an "instruction set" that guides the flowing solution to specific sites in the system. Such systems can operate as sensors that indicate concentrations of reactants through measurement of the trajectory along which the flow demonstrates a maximal speed. The performed simulations suggest that the solutal buoyancy mechanism causes fluid motion and is responsible for all of the observed effects. More broadly, our studies provide a new route for forming self-organizing flow systems that can yield fundamental insight into nonequilibrium, dynamical systems.

Efficient motion estimation and discrete cosine transform implementation using the graphics processing units.

Motion Estimation (ME) and the two-dimensional (2D) discrete cosine transform (2D-DCT) are both computationally expensive parts of HEVC standard, therefore real-time performance of the HEVC may not be free from glitches. To address this issue, this study deploys the graphics processing units (GPUs) to perform the ME and 2D-DCT tasks. In this concern, authors probed into four levels of parallelism (i.e., frame, macroblock, search area, and sum of the absolute difference (SAD) levels) existing in ME. For comparative analysis, authors involved full search (FS), test zone search (TZS) of HEVC, and hierarchical diamond search (EHDS) ME algorithms. Similarly, two levels of parallelism (i.e., macroblock and sub-macroblock) are also explored in 2D-DCT. Notably, the least computationally complex multithreaded Loeffler DCT algorithm is utilized for computing 2D-DCT. Experimental results show that ME processing task corresponding to 25 frames, with each frame of size (3840×2160) pixels, is accomplished in 0.15 seconds on the NVIDIA GeForce GTX 1080, whereas the 2D-DCT task along with the image reconstruction and differencing corresponding to 25 frames took 0.1 seconds. Collectively, both ME and 2D-DCT tasks are processed in 0.25 seconds, which still leaves enough room for the encoder's remaining parts to be executed within one second. Due to this enhancement, the resultant encoder can safely be used in real-time applications.

Framework for charged compact objects admitting conformal motion in higher dimension.

We propose a new framework for spherical charged compact objects admitting conformal motion in five-dimensional spacetime. The outer spacetime is considered as Reissner-Nordström to obtain matching conditions. The behavior of model characteristics like stress, pressure, and surface tension for the specific density profile is investigated by using Einstein's Maxwell field equations in a five-dimensional framework. For the proposed solution, all physical parameters behave very well even for variations in electric charge parameters. The existence of charged compact stars is also predicted by this study.

Unpacking Galvanic Vestibular Stimulation using simulations and relating current flow to reported motions: Comparison across common and specialized electrode placements.

BACKGROUND: Galvanic Vestibular Stimulation (GVS) is a non-invasive electrical stimulation technique that is typically used to probe the vestibular system. When using direct current or very low frequency sine, GVS causes postural sway or perception of illusory (virtual) motions. GVS is commonly delivered using two electrodes placed at the mastoids, however, placements involving additional electrodes / locations have been employed. Our objective was to systematically evaluate all known GVS electrode placements, compare induced current flow, and how it relates to the archetypal sway and virtual motions. The ultimate goal is to help users in having a better understanding of the effects of different placements. METHODS: We simulated seven GVS electrode placements with same total injected current using an ultra-high resolution model. Induced electric field (EF) patterns at the cortical and the level of vestibular organs (left and right) were determined. A range of current flow metrics including potential factors such as inter-electrode separation, percentage of current entering the cranial cavity, and symmetricity were calculated. Finally, we relate current flow to reported GVS motions. RESULTS: As expected, current flow patterns are electrode placement specific. Placements with two electrodes generally result in higher EF magnitude. Placements with four electrodes result in lower percentage of current entering the cranial cavity. Symmetric placements do not result in similar EF values in the left and the right organs respectively- highlighting inherent anatomical asymmetry of the human head. Asymmetric placements were found to induce as much as ~3-fold higher EF in one organ over the other. The percentage of current entering the cranial cavity varies between ~15% and ~40% depending on the placement. CONCLUSIONS: We expect our study to advance understanding of GVS and provide insight on probable mechanism of action of a certain electrode placement choice. The dataset generated across several metrics will support hypothesis testing relating empirical outcomes to current flow patterns. Further, the differences in current flow will guide stimulation strategy (what placement and how much scalp current to use) and facilitate a quantitatively informed rational / optimal decision.

Comprehensive modeling of corkscrew motion in micro-/nano-robots with general helical structures.

Micro-/nano-robots (MNRs) have impressive potential in minimally invasive targeted therapeutics through blood vessels, which has disruptive impact to improving human health. However, the clinical use of MNRs has yet to happen due to intrinsic limitations, such as overcoming blood flow. These bottlenecks have not been empirically solved. To tackle them, a full understanding of MNR behaviors is necessary as the first step. The common movement principle of MNRs is corkscrew motion with a helical structure. The existing dynamic model is only applicable to standard helical MNRs. In this paper, we propose a dynamic model for general MNRs without structure limitations. Comprehensive simulations and experiments were conducted, which shows the validity and accuracy of our model. Such a model can serve as a reliable basis for the design, optimization, and control of MNRs and as a powerful tool for gaining fluid dynamic insights, thus accelerating the development of the field.

Low-latitude mesopelagic nutrient recycling controls productivity and export.

Low-latitude (LL) oceans account for up to half of global net primary production and export1-5. It has been argued that the Southern Ocean dominates LL primary production and export6, with implications for the response of global primary production and export to climate change7. Here we applied observational analyses and sensitivity studies to an individual model to show, instead, that 72% of LL primary production and 55% of export is controlled by local mesopelagic macronutrient cycling. A total of 34% of the LL export is sustained by preformed macronutrients supplied from the Southern Ocean via a deeper overturning cell, with a shallow preformed northward supply, crossing 30° S through subpolar and thermocline water masses, sustaining only 7% of the LL export. Analyses of five Coupled Model Intercomparison Project Phase 6 (CMIP6) models, run under both high-emissions low-mitigation (shared socioeconomic pathway (SSP5-8.5)) and low-emissions high-mitigation (SSP1-2.6) climate scenarios for 1850-2300, revealed significant across-model disparities in their projections of not only the amplitude, but also the sign, of LL primary production. Under the stronger SSP5-8.5 forcing, with more substantial upper-ocean warming, the CMIP6 models that account for temperature-dependent remineralization promoted enhanced LL mesopelagic nutrient retention under warming, with this providing a first-order contribution to stabilizing or increasing, rather than decreasing, LL production under high emissions and low mitigation. This underscores the importance of a mechanistic understanding of mesopelagic remineralization and its sensitivity to ocean warming for predicting future ecosystem changes.

Automatic motion estimation with applications to hiPSC-CMs.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Conceptualization of Cloud-Based Motion Analysis and Navigation for Wearable Robotic Applications.

The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects.