Real-time Motion Research Articles

Interpretive Methods as Policy Tools: The COVID-19 Migration Crisis in India and the Case for Policy Innovation

Attempts to quantify and measure migration in India have proved inadequate as far as policy change is concerned. The Census of India 2011 and the National Sample Surveys (particularly the 64th Round conducted between 2007 and 2008), while useful, are too infrequent and temporally limited to offer insights into real-time movements and seasonal migration. On the other hand, even when data is available, it can be difficult to use it to understand future patterns and reactions to policy changes, as the COVID-19 pandemic-induced crisis showed. It was not just the number of migrants residing in major cities that was unanticipated. The government and local authorities also failed to anticipate the impetus for migrant labourers to move back to their hometowns once their revenue streams were cut off. This suggests that the government had not considered the subjective, emotional concerns that migrants had about their well-being, and consequently failed to predict how they would react. This article argues that a paradigm shift towards a deeper, more wide-ranging use of qualitative-interpretive methods is in order if we are to obtain a fuller picture of migrant needs and actions. This qualitative data may then be used in tandem with surveys, enumerations and quantitative studies to predict both the quantum and the direction of migrant movements, actions and reactions. These methods are, therefore, not just supplementary, but critical policy tools in themselves, allowing policymakers to craft interventions and responses that account for the subjective considerations surrounding migrant mobility.

Altered foot placement modulation with somatosensory stimulation in people with chronic stroke

Many individuals who experience a stroke exhibit reduced modulation of their mediolateral foot placement, an important gait stabilization strategy. One factor that may contribute to this deficit is altered somatosensory processing, which can be probed by applying vibration to the involved muscles (e.g., the hip abductors). The purpose of this study was to investigate whether appropriately controlled hip abductor vibration can increase foot placement modulation among people with chronic stroke. 40 people with chronic stroke performed a series of treadmill walking trials without vibration and with vibration of either the hip abductors or lateral trunk (a control condition) that scaled with their real-time mediolateral motion. To assess participants’ vibration sensitivity, we also measured vibration detection threshold and lateral sway evoked by abductor vibration during quiet standing. As a group, foot placement modulation increased significantly with either hip or trunk vibration, compared to without vibration. However, these changes were quite variable across participants, and were not predicted by either vibration detection threshold or the lateral sway evoked by hip vibration during standing. Overall, we found that somatosensory stimulation had small, positive effects on post-stroke foot placement modulation. Unexpectedly, these effects were observed with both hip abductor and lateral trunk vibration, perhaps indicating that the trunk can also provide useful somatosensory feedback during walking. Future work is needed to determine whether repeated application of such somatosensory stimulation can produce sustained effects on this important gait stabilization strategy.

Exploring injury assessment using motion tracking technology and ergonomic tools in archery performance

Introduction: Archers face a notable annual risk of injury attributed to the repetitive strain exerted on their upper body muscles and joints. This groundbreaking study marks the first utilization of real-time motion tracking combined with ergonomic analysis in archery research, eliminating the need for extensive equipment setups and streamlining data collection.Methods: Thirteen participants were involved, undertaking four tasks with varying draw weights of bows and target positions.Results: The precise recording of human movements revealed distinctive postures adopted by both genders across different tasks. Furthermore, an assessment of exposed spinal force and its correlation with anatomical variables was conducted, providing valuable insights into injury risks during archery performances.

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.

As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included "radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network". Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

Digital Light Processing 4D Printing of Poloxamer Micelles for Facile Fabrication of Multifunctional Biocompatible Hydrogels as Tailored Wearable Sensors.

The lack of both digital light processing (DLP) compatible and biocompatible photopolymers, along with inappropriate material properties required for wearable sensor applications, substantially hinders the employment of DLP 3D printing in the fabrication of multifunctional hydrogels. Herein, we discovered and implemented a photoreactive poloxamer derivative, Pluronic F-127 diacrylate, which overcomes these limitations and is optimized to achieve DLP 3D printed micelle-based hydrogels with high structural complexity, resolution, and precision. In addition, the dehydrated hydrogels exhibit a shape-memory effect and are conformally attached to the geometry of the detection point after rehydration, which implies the 4D printing characteristic of the fabrication process and is beneficial for the storage and application of the device. The excellent cytocompatibility and in vivo biocompatibility further strengthen the potential application of the poloxamer micelle-based hydrogels as a platform for multifunctional wearable systems. After processing them with a lithium chloride (LiCl) solution, multifunctional conductive ionic hydrogels with antifreezing and antiswelling properties along with good transparency and water retention are easily prepared. As capacitive flexible sensors, the DLP 3D printed micelle-based hydrogel devices exhibit excellent sensitivity, cycling stability, and durability in detecting multimodal deformations. Moreover, the DLP 3D printed conductive hydrogels are successfully applied as real-time human motion and tactile sensors with satisfactory sensing performances even in a -20 °C low-temperature environment.

MWCNT-reinforced silicone's dual response to human and mechanical motion: Exploring strain sensing behavior and practical applications

This study investigates the human motion sensing capabilities of silicone rubber (SR) composites reinforced with multi-walled carbon nanotubes (MWCNTs). It focuses on assessing the responsiveness of the composites to both compressive and tensile deformations, replicating motions from machines to human movements. The analysis underscores the enhanced sensitivity of the 3 parts per hundred (phr) compressive sample, with gauge factors of 7.5 and 14.44 at distinct linear ranges under 30% strain. During forefinger and thumb pressing, the relative change in resistance was 160 and 350, respectively. Additionally, the study examines the 5 phr sample, revealing a significant 346.67% increase in relative resistance changes during the initial cyclic loading phase under 30% tensile deformation compared to the compression sample. The mechanical properties are also evaluated, showing the 5 phr compressive sample possessing a compressive modulus 180.09% higher than the modulus of the unfilled specimen (1.246 MPa), along with a hysteresis loss of 37.24 kJ. Furthermore, the tensile modulus of the 5 phr specimen exceeds that of the unfilled rubber by 218.84%. The developed composites hold promise for creating sensors capable of tracking athletes' movements in real time, offering immediate performance feedback and post-processing analysis. Moreover, these materials could contribute to the design of rehabilitation devices aimed at assisting individuals with hand injuries in regaining their agility and mobility.

Wearable Sensor Based on a Tough Conductive Gel for Real-Time and Remote Human Motion Monitoring.

The usage of a conductive hydrogel in wearable sensors has been thoroughly researched recently. Nonetheless, hydrogel-based sensors cannot simultaneously have excellent mechanical property, high sensitivity, comfortable wearability, and rapid self-healing performance, which result in poor durability and reusability. Herein, a robust conductive hydrogel derived from one-pot polymerization and subsequent solvent replacement is developed as a wearable sensor. Owing to the reversible hydrogen bonds cross-linked between polymer chains and clay nanosheets, the resulting conductive hydrogel-based sensor exhibits outstanding flexibility, self-repairing, and fatigue resistance performances. The embedding of graphene oxide nanosheets offers an enhanced hydrogel network and easy release of wearable sensor from the target position through remote irradiation, while Li+ ions incorporated by solvent replacement endow the wearable sensor with low detection limit (sensing strain: 1%), high conductivity (4.3 S m-1) and sensitivity (gauge factor: 3.04), good freezing resistance, and water retention. Therefore, the fabricated wearable sensor is suitable to monitor small and large human motions on the site and remotely under subzero (-54 °C) or room temperature, indicating lots of promising applications in human-motion monitoring, information encryption and identification, and electronic skins.

Sensing analysis of self-mixing and Michelson interferometry with neural-network-based phase extraction

Self-mixing interference (SMI) and Michelson interference (MI) with the phase extraction method based on a neural network (NN) for sensing is analyzed in this work. Since MI and SMI signals are predominantly a cosine-like function of optical phase, the NN’s core task involves mapping the cosine function to phase values, which is solved by a tectonic characteristic function. Experimental results show that the movement information carried by MI and SMI signals can be accurately measured by the NN with a phase sensitivity of 10−3 rad, yielding outcomes that align with the simulations. Especially, algorithms are essential for extracting real-time motion direction of objects in MI. The simplicity of real-time motion direction extraction is the key advantage of SMI, while further utilization of variational mode decomposition is necessary to mitigate the impact of periodic optical frequency variation. This work offers a potentially valuable contribution to develop interferometry for engineering applications.

Liquid Metal Composites-Enabled Real-Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy.

Prosthetic hands play a vital role in restoring forearm functionality for patients who have suffered hand loss or deformity. The hand gesture intention recognition system serves as a critical component within the prosthetic hand system. However, accurately and swiftly identifying hand gesture intentions remains a challenge in existing approaches. Here, a real-time motion intention recognition system utilizing liquid metal composite sensor bracelets is proposed. The sensor bracelet detects pressure signals generated by forearm muscle movements to recognize hand gesture intent. Leveraging the remarkable pressure sensitivity of liquid metal composites and the efficient classifier based on the optimized recognition algorithm, this system achieves an average offline and real-time recognition accuracy of 98.2% and 92.04%, respectively, with an average recognition speed of 0.364 s. Thus, this wearable system shows advantages in superior recognition speed and accuracy. Furthermore, this system finds applications in master-slave control of prosthetic hands in unmanned scenarios, such as electrically powered operations, space exploration, and telemedicine. The proposed system promises significant advances in next-generation intent-controlled prosthetic hands and robots.

Real-time motion force-feedback system with predictive-vision for improving motor accuracy

Many haptic guidance systems have been studied over the years; however, most of them have been limited to predefined guidance methods. Calculating guidance according to the operator’s motion is important for efficient human motor adaptation and learning. In this study, we developed a system that haptically provides guidance trajectory by sequential weighting between the operator’s trajectory and the ideal trajectory calculated from a predictive-vision system. We investigated whether motion completion with a predictive-vision system affects human motor accuracy and adaptation in time-constrained goal-directed reaching and ball-hitting tasks through subject experiments. The experiment was conducted with 12 healthy participants, and all participants performed ball-hitting tasks. Half of the participants get forceful guidance from the proposed system in the middle of the experiment. We found that the use of the proposed system improved the operator’s motor performance. Furthermore, we observed a trend in which the improvement in motor performance using this system correlated with that after the washout of this system. These results suggest that the predictive-vision system effectively enhances motor accuracy to the target error in dynamic and time-constrained reaching and hitting tasks and may contribute to facilitating motor learning.

Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character

Virtual characters are now widely used in games, computer-generated (CG) movies, virtual reality (VR), and communication media. The continued technological innovations in motion capture mean that a more natural representation of a three-dimensional character’s motion should be achievable. Many researchers have investigated how virtual characters interact with their surrounding environment through spatial relationships, which were introduced for adapting and preserving character motion. However, technical problems should be resolved to enable the control of characters in augmented reality (AR) environments that combine with the real world, and this can be achieved by adapting motion to environmental differences using original motion datasets. In this paper, we investigate a novel method for preserving automatic motion adaptation for a virtual character in AR environments. We used specific object (e.g., puddle) recognition and the spatial properties of the user’s surrounding space, e.g., object types and positions, and ran validation experiments to provide accurate motion to improve the AR experience. Our experimental study showed positive results in terms of smooth motion in AR configurations. We also found that the participants using AR felt a greater sense of co-presence with the character through adapted motion.

Ultra-hypofractionated prostate cancer radiotherapy: Dosimetric impact of real-time intrafraction prostate motion and daily anatomical changes

PurposeTo retrospectively assess the differences between planned and delivered dose during ultra-hypofractionated (UHF) prostate cancer treatments, by evaluating the dosimetric impact of daily anatomical variations alone, and in combination with prostate intrafraction motion. MethodsProstate intrafraction motion was recorded with a transperineal ultrasound probe in 15 patients treated by UHF radiotherapy (36.25 Gy/5 fractions). The dosimetric objective was to cover 99 % of the clinical target volume with the 100 % prescription isodose line. After treatment, planning CT (pCT) images were deformably registered onto daily Cone Beam CT to generate pseudo-CT for dose accumulation (accumulated CT, aCT). The interplay effect was accounted by synchronizing prostatic shifts and beam geometry. Finally, the shifted dose maps were accumulated (moved-accumulated CT, maCT). ResultsNo significant change in daily CTV volumes was observed. Conversely, CTV V100% was 98.2 ± 0.8 % and 94.7 ± 2.6 % on aCT and maCT, respectively, compared with 99.5 ± 0.2 % on pCT (p < 0.0001). Bladder volume was smaller than planned in 76 % of fractions and D5cc was 33.8 ± 3.2 Gy and 34.4 ± 3.4 Gy on aCT (p = 0.02) and maCT (p = 0.01) compared with the pCT (36.0 ± 1.1 Gy). The rectum was smaller than planned in 50.3 % of fractions, but the dosimetric differences were not statistically significant, except for D1cc, found smaller on the maCT (33.2 ± 3.2 Gy, p = 0.02) compared with the pCT (35.3 ± 0.7 Gy). ConclusionsAnatomical variations and prostate movements had more important dosimetric impact than anatomical variations alone, although, in some cases, the two phenomena compensated. Therefore, an efficient IGRT protocol is required for treatment implementation to reduce setup errors and control intrafraction motion.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.

Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scanmotion. To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in thescanner. We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIMvalues. The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-basedreconstructions.

A liquid metal/polypyrrole electrospun TPU composite conductive network for highly sensitive strain sensing in human motion monitoring.

Developing soft wearable sensors with high sensitivity, low cost, and a wide monitoring range is crucial for monitoring human health. Despite advances in strain sensor technology, achieving high sensitivity and a wide operating range in a single device remains a major challenge in its design and preparation. Herein, a liquid metal (LM) is innovatively ultrasonically anchored to the gaps and surfaces of thermoplastic polyurethane (TPU) electrospun fibers, and then a conductive pathway is constructed through polypyrrole (PPy) self-polymerization to prepare a composite film. The strain sensor developed by ultrasonic anchoring and original polymerization technology shows a high strain coefficient (GF = 4.36 at 12.5% strain) and a low detection limit (less than 1% strain). Importantly, this sensor can monitor joint motion and subtle skin deformations in real time. In addition, the integration of strain sensors and N95 masks enables real-time monitoring of human respiration.

MPTree: A Sampling-based Vehicle Motion Planner for Real-time Obstacle Avoidance

This paper presents a novel and modular framework, named MPTree, for real-time vehicle motion planning with dynamic obstacle avoidance. MPTree adopts a sampling-based algorithm, to explore a tree of Motion Primitives (MPs) and return near-optimal trajectories. Specifically, MPTree builds motion primitives to connect the tree nodes (waypoints), sampled in a mesh of waylines on the local planning horizon. The tree exploration is based on a semi-structured RRTa, with an application-specific cost function (e.g., minimum-jerk or minimum-time) and high-level behavioral policy. We show examples of MPTREE’s specialization for urban environments and autonomous racing, using fast-to-evaluate motion primitives to accelerate the tree exploration phase. A prototype implementation is tested in a closed-loop simulation environment. Our preliminary results show that MPTree provides feasible collision-free trajectories while ensuring low computational times.

Combining closed-form and numerical solutions for the inverse kinematics of six-degrees-of-freedom collaborative handling robot

In the process of solving the inverse kinematics of six-degrees-of-freedom collaborative robots, the numerical solution has problems such as low accuracy and singular configurations. Moreover, due to the high coupling of its position and attitude, the direct closed-form solution fails. To address these problems, an inverse kinematics algorithm that combines closed-form and numerical solutions was proposed. The Jacobian matrix was established based on the forward kinematics equation of the six-degrees-of-freedom collaborative robot. Its inverse matrix was obtained by a singular value decomposition of the matrix using the Manocha elimination method to avoid the singularities of the Jacobian matrix. The optimal inverse kinematics solution was obtained using the Newton–Raphson iterative method. A computer simulation implemented in MATLAB and Visual C++ was used to evaluate the accuracy and speed of the proposed algorithm. The results of the trajectory planning experiments demonstrate that the algorithm satisfies the requirements for real-time smooth motion. This study of inverse kinematics lays the theoretical foundation for solutions to the inverse problem and real-time control of collaborative robots.

RAMPAGE: Toward Whole-Body, Real-Time, and Agile Motion Planning in Unknown Cluttered Environments for Mobile Manipulators

RAMPAGE: Toward Whole-Body, Real-Time, and Agile Motion Planning in Unknown Cluttered Environments for Mobile Manipulators

Clinical experience with adaptive MRI-guided pancreatic SBRT and the use of abdominal compression to reduce treatment volume.

This work presents a method to treat stereotactic body radiation therapy (SBRT) for pancreatic cancer on a magnetic resonance-guided linear accelerator (MR-linac) using daily adaptation, real-time motion monitoring, and abdominal compression. The motion management and treatment planning process involves a magnetic resonance imaging (MRI) simulation with cine and 3D images, a computed tomography (CT) simulation with a breath-hold CT and a 4DCT, pre-treatment verification and planning MRI, and intrafraction MRI cine images. The results from 26 patients were included in this work. Our motion management process results in consistent motion analysis on the CT simulation, MRI simulation, and each treatment fraction. The liver dome was found to be an overestimate of tumor superior/inferior (SI) motion for most patients. Adding compression reduced SI liver dome motion by 6.2 mm on average. Clinical outcomes are similar to those observed in the literature. In this work, we demonstrate how pancreatic SBRT can be successfully treated on an MR-linac using abdominal compression. This allows for an increased duty cycle compared to gating and/or breath-hold techniques.

Application of Real-time Motion Capture Technology in Street Dance Movement Analysis and Optimization

Abstract With the advancement of motion capture technology, its application in dance movement training has become increasingly prevalent. This study explores the use of real-time dynamic capture technology for the analysis and optimization of street dance movements. A street dance movement system based on real-time motion capture technology is designed. Initially, the data obtained from sensors are fused using the Extended Kalman Filtering gesture fusion algorithm to identify street dance movements. Subsequently, the gestures of the street dance movements are matched with template movements using the DTW algorithm, facilitating movement optimization. Finally, the system is scrutinized for its performance and application analysis. The number of concurrent users of the system is in the interval of [90,99], and the average response time for uploading street dance moves and street dance move evaluation is 7.631s and 0.35s, respectively, which basically meets the design objectives. The maximum error, average absolute error, and root mean square error of the pose angles solved by the algorithm in this paper do not exceed ±1.61°, 0.20°, and 0.25°, respectively, and the algorithm is highly accurate and smooth, which meets the requirements of pose solving. Through the evaluation of 12 training learning results of 6 testers, it is found that the movements of the testers are more and more similar to the template movements after several training sessions, which verifies that the system in this paper can guide the learners to learn and optimize the street dance movements.

MFNet:Real-Time Motion Focus Network for Video Frame Interpolation

MFNet:Real-Time Motion Focus Network for Video Frame Interpolation