Motion Tracking Research Articles

Evaluation of lower-body gait kinematics on outdoor surfaces using wearable sensors.

The effects of outdoor surfaces on gait are unclear due to difficulties associated with motion tracking outside laboratories. Today, inertial measurement unit (IMU) systems can be deployed to understand the biomechanical adaptations required to navigate real-world environments successfully. This study used IMUs devices to identify lower-limb kinematic adaptations while walking on outdoor surfaces. We hypothesize that gait adaptations between surface types will present as differences in lower-limb joint angles. Thirty able-bodied adults performed walking trials with IMUs on the lower back, thighs, and shanks. Outdoor walking surfaces were flat and even (flateven) (0° grade cement), cobblestone, grass, slope up, slope down, stairs up, and stairs down. A complementary-based sensor fusion algorithm was used to compute hip and knee joint flexion-extension angles, and data were normalized to 100% of the gait cycle based on foot-strike events. Flateven walking was compared against all other surfaces. Two-sample one-dimensional statistical parametric mapping (1d-SPM) t-tests were used to identify differences between angles (α≤0.05). Significant differences in joint angles were identified when grass, slope up, slope down, stairs up, and stairs down walking were compared with flateven (p≤0.005). Moreover, differences were found between slope and stair conditions (p≤0.004). No significant differences were noted between flateven and cobblestone. This study demonstrates that gait adaptations driven by differences in surface types can be observed using IMU sensors in an outdoor setting.

You Only Look Once Version 5 and Deep Simple Online and Real-Time Tracking Algorithms for Real-Time Customer Behavior Tracking and Retail Optimization

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems.

Enhancing cell motility via non-contact capacitively coupled electrostatic field.

Cellular motility is essential for making and maintaining multicellular organisms throughout their lifespan. Migrating cells can move either individually or collectively by a crawling movement that links the cytoskeletal activity to the adhesion surface. In vitro stimulation by electric fields can be achieved by direct, capacitive or inductive coupled setups. We tested the effects of electrical stimulation provided by capacitive coupling on glioma cells, using a capacitive-coupled system powered by a potential difference of 35V between two electrodes placed outside the culture dish. Numerical dosimetry identified two different fields: (i) in the order of 103 V/m at the level of the dielectric substrates, with almost uniform distribution; (ii) in the order of 10-1 V/m at the level of the culture medium, with spatial and material-dependent distribution. The scratch assay and the tracking of single-cell movement showed a boosted motility when crawling occurs on polystyrene surfaces, demonstrating the feasibility of this peculiar exposure system to generate forces capable of influencing cell behavior.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Evaluating manufacturing plants readiness for distributed ledger technology: a network framework for ambiguity levels

The utilisation of blockchain technology has gained significant traction within contemporary supply chains owing to its ability to enhance transparency, security, and traceability. Manufacturing plants, as pivotal components of the supply chain, stand to benefit from improved tracking and transparency of goods movement, real-time visibility, quality control processes, and adherence to industry standards through blockchain implementation. Nonetheless, without a comprehensive assessment of manufacturing plants’ readiness to embrace blockchain technology, the anticipated benefits may give way to unforeseen challenges. In this study, a novel network framework is offered to evaluate manufacturing plants’ readiness for adopting distributed ledger technology, specifically blockchain, under varying levels of ambiguity, including high (fuzzy) and low (scenario) ambiguity. This framework is distinguished by its ability to address uncertainty in evaluations, incorporating both scenario-based and fuzzy programming approaches. Furthermore, the framework treats evaluation criteria as interconnected entities, fostering a network perspective rather than a black-box approach. The proposed framework is then validated through a case study involving five manufacturing plants and twenty-four evaluation criteria. Our findings underscore the pivotal role of uncertainty considerations in ranking manufacturing plants, with the fifth plant emerging as the frontrunner across both fuzzy and scenario-based assessments in most instances.

Neural networks empowered: a machine learning-enabled, Gyro mmID for enhanced virtual reality and motion tracking applications

Abstract With the emerging developments in millimeter-wave/5G technologies, the potential for wireless Internet of things devices to achieve widespread sensing, precise localization, and high data-rate communication systems becomes increasingly viable. The surge in interest surrounding virtual reality (VR) and augmented reality (AR) technologies is attributed to the vast array of applications they enable, ranging from surgical training to motion capture and daily interactions in VR spaces. To further elevate the user experience, and real-time and accurate orientation detection of the user, the authors proposes the utilization of a frequency-modulated continuous-wave (FMCW) radar system coupled with an ultra-low-power, sticker-like millimeter-wave identification (mmID). The mmID features four backscattering elements, multiplexed in amplitude, frequency, and spatial domains. This design utilizes the training of a supervised learning classification convolutional neural network, enabling accurate real-time three-axis orientation detection of the user. The proposed orientation detection system exhibits exceptional performance, achieving a noteworthy accuracy of 90.58% over three axes at a distance of 8 m. This high accuracy underscores the precision of the orientation detection system, particularly tailored for medium-range VR/AR applications. The integration of the FMCW-based mmID system with machine learning proves to be a promising advancement, contributing to the seamless and immersive interaction within virtual and augmented environments.

Time Domain Design of a Marine Target Tracking System Accounting for Environmental Disturbances

Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating realistic environmental disturbances. Three guidance laws and four key performance indicators are analysed to evaluate system performance under disturbed and ideal conditions. A robust and systematic evaluation pipeline is developed and applied to a case study featuring a scaled tugboat model. This approach provides a reliable method to assess tracking accuracy and robustness in adverse conditions. The results are selected from a wide range of possibilities to show the effect of the disturbances on the selected target tracking motion control scenario with two manoeuvres and two environmental conditions. The results are measured through the selected key performance indicators, and several phases are identified for each manoeuvre to extend the analysis not only to the global KPI values but also to the partial values of defined phases. They reveal the quantitative effects of environmental disturbances, exposing different system behaviours and trends. These findings demonstrate the effectiveness of the proposed pipeline in quantifying tracking system performance, delivering useful understandings of the system under environmental disturbances. The broader implications of this study are substantial, offering enhanced predictive accuracy for the performance of the analysed systems, particularly in the context of target tracking. Furthermore, introducing numerical key performance indicators facilitates a more rigorous comparison of different system characteristics, enabling informed decisions in designing and optimising autonomous operations in challenging environments.

Facial Movements Extracted from Video for the Kinematic Classification of Speech

Speech Sound Disorders (SSDs) are prevalent communication problems in children that pose significant barriers to academic success and social participation. Accurate diagnosis is key to mitigating life-long impacts. We are developing a novel software solution—the Speech Movement and Acoustic Analysis Tracking (SMAAT) system to facilitate rapid and objective assessment of motor speech control issues underlying SSD. This study evaluates the feasibility of using automatically extracted three-dimensional (3D) facial measurements from single two-dimensional (2D) front-facing video cameras for classifying speech movements. Videos were recorded of 51 adults and 77 children between 3 and 4 years of age (all typically developed for age) saying 20 words from the mandibular and labial-facial levels of the Motor-Speech Hierarchy Probe Wordlist (MSH-PW). Measurements around the jaw and lips were automatically extracted from the 2D video frames using a state-of-the-art facial mesh detection and tracking algorithm, and each individual measurement was tested in a Leave-One-Out Cross-Validation (LOOCV) framework for its word classification performance. Statistics were evaluated at the α=0.05 significance level and several measurements were found to exhibit significant classification performance in both the adult and child cohorts. Importantly, measurements of depth indirectly inferred from the 2D video frames were among those found to be significant. The significant measurements were shown to match expectations of facial movements across the 20 words, demonstrating their potential applicability in supporting clinical evaluations of speech production.

A Finite-Time Disturbance Observer for Tracking Control of Nonlinear Systems Subject to Model Uncertainties and Disturbances

In this study, a finite-time disturbance observer (FTDOB) with a new structure is originally put forward for the motion tracking problem of a class of nonlinear systems subject to model uncertainties and exogenous disturbances. Compared to existing disturbance estimator designs in the literature, in which the estimation error only converges to the origin asymptotically under assumptions that the first and/or second derivatives are vanishing, the suggested DOB is able to estimate the disturbance exactly in finite time. Firstly, uncertainties (parametric and unstructured uncertainties), unknown dynamics, and external disturbances in system dynamics are lumped into a generalized disturbance term that is subsequently estimated by the proposed DOB. Based on this, a DOB-based backstepping controller is synthesized to ensure high-accuracy tracking performance under various working conditions. The stability analysis of not only the DOB but also the overall closed-loop system is theoretically confirmed by the Lyapunov stability theory. Finally, the advantages of the proposed FTDOB and the FTDOB-based controller over other DOBs and existing DOB-based controllers are explicitly simultaneously demonstrated by a series of numerical simulations on a second-order mechanical system and comparative experiments on an actual DC motor system.

Eyelid Motion Tracking During Blinking Using High-Speed Imaging and Digital Image Correlation.

This study presents a novel technique to measure the motion of the eyelid during blinking. High-speed imaging and digital image correlation (DIC) were employed to monitor the eyelid during blinking in a non-invasive manner. Both spontaneous and reflex blinks were studied. A black liquid eyeliner was used to generate a speckle pattern on the surface of the eyelid. Facet motion captured through a DIC analysis software generated kinematic data for each blink. Calculations using this data set yielded information on the duration of the blink, eyelid displacements, and peak eyelid velocities. A consistent data set quantified the difference between blink types and reinforced the accuracy and repeatability of this DIC analysis method to measure the kinematics of blinking.

Mapping the Landscape of Biomechanics Research in Stroke Neurorehabilitation: A Bibliometric Perspective

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors.

Monitoring Dairy Cow Rumination Behavior Based on Upper and Lower Jaw Tracking

To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization, establishing it as a detector of the upper and lower jaws of dairy cows. Subsequently, the DeepSort tracking algorithm was utilized to track the upper and lower jaws and plot their movement trajectories. By calculating the difference between the centroid coordinates of the tracking boxes for the upper and lower jaws during rumination, the rumination curve was obtained. Finally, the number of rumination chews and the false detection rate were calculated. The system successfully monitored the frequency of the cows’ chewing actions during rumination. The experimental results indicate that the enhanced network model achieved a mean average precision (mAP@0.5) of 97.5% and 97.9% for the upper and lower jaws, respectively, with precision (P) of 95.4% and 97.4% and recall (R) of 97.6% and 98.4%, respectively. Two methods for determining chewing were proposed, which showed false detection rates of 8.34% and 3.08% after the experimental validation. The research findings validate the feasibility of the jaw movement tracking method, providing a reference for the real-time monitoring of the rumination behavior of dairy cows in group housing environments.

Analyze the physical interaction between the user and the furniture design to optimize comfort and functionality

The physical interaction between users and furniture is pivotal in determining comfort and functionality, particularly in environments where individuals spend extended periods, such as offices, homes, and public spaces. This study aims to analyze how different furniture designs impact user comfort, postural stability, and long-term usability. By employing a hybrid research framework combining observational studies, simulations, and advanced technological tools such as motion tracking, pressure mapping, and biomechanical sensors, the research provides a comprehensive evaluation of user-furniture interaction. The study involved 178 participants with diverse demographic backgrounds, allowing for a broad range of body types, ages, and activity levels to be examined. Key findings indicate that ergonomic features such as adjustable seat height, lumbar support, and reclining mechanisms significantly enhance comfort, particularly when customized to the user’s anthropometric profile. For example, adjustable seat height reduced pressure on the thighs, improving comfort by 8.5% over prolonged periods. Additionally, lumbar support was the most compelling feature in alleviating muscle strain, improving overall comfort by 9.0%. The analysis of long-term comfort revealed that postures supporting dynamic movements, such as using a standing desk, maintained higher comfort levels over time compared to static postures like leaning forward, which showed a marked increase in muscle fatigue. Postural stability analysis showed that sitting at a 90° angle provided the best balance of stability and long-term comfort, with a usability rating 8.4. In contrast, leaning forward exhibited the lowest postural stability and the highest discomfort, making it unsuitable for prolonged tasks.

Innovation in physical education teaching based on biomechanics feedback: Design and evaluation of personalized training programs

Psychometric training is a learning and development process that is tied to the requirements and interests of individual learners. The effectiveness and the fun of workout practices can be greatly enhanced by using more personal methods in training. These programs entail tailoring workouts to the fitness level and aims of every participant, as well as their perception of the workout plans. The suggestion of this investigation is to develop and evaluate the effectiveness of individual physical training for teachers focused on biomechanics feedback. Proper body alignment is crucial during any exercise to avoid injuries and achieve maximum results; it is always difficult to sustain correct posture. For motion tracking and to give real-time biomechanics feedback to the students in this study, a refined convolutional neural network (RCNN) has been presented. Filming was done using high-speed cameras and motion capture systems to capture biomechanical responses, which included joint angle, muscle activation patterns, and body posture during activities. This study involved 158 participants drawn from different learning institutions. This approach offers rational and specific feedback on the postures of the body; it enables people to correct themselves and sustain motivation without engaging a trainer. In this study, participants with different levels of fitness engaged with the interactive system were compared to the traditional training method. The result indicated a positive shift in the delivery of personalized training with biomechanics feedback on the system’s potential teaching aid in physical education classes. The study, thus underscores the importance of technology supporting change in physical education programs to improve the student’s learning experiences and their performance in exercises.

Development of quantitative PET/MR imaging for measurements of hepatic portal vein input function: a phantom study

BackgroundAccurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.MethodsA custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.ResultsReconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.ConclusionThe proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.

Nose-Driven Cursor Control: An Assistive System for Disabled Individuals Using OCRM Similarity Tracking

This paper introduces a visual mouse system designed to assist disabled individuals in controlling computer mouse functions, such as cursor movement and clicking actions, using simple body gestures. Specifically, the system tracks the user’s nose tip to execute mouse commands, providing an untouchable alternative for desktop interaction. The main goal is to create an accessible, low-cost solution using widely available hardware to make computing more inclusive for individuals with physical disabilities. Using a camera, the system captures and translates user head movements into mouse gestures on the screen. The proposed methodology is built around visual tracking, isolating a sample region from each video frame and then identifying that region in subsequent frames through Optimal Cost Region Matching (OCRM). This similarity measure ensures accurate and seamless tracking of the user's movement, providing an effective control mechanism. Experimental results demonstrate that the OCRM-based visual mouse system outperforms existing Camera Mouse applications regarding reliability and efficiency. The proposed approach delivers a flexible, cost-effective, and consistent solution for enhancing computer accessibility for disabled users.

The Trail Making Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

Motion tracking with automated pose estimator can enhance ankle-foot-orthoses alignment

BackgroundAnkle-foot orthoses are commonly prescribed to address motor impairments in individuals with neurological disorders. Proper alignment of Ankle-foot orthoses, commonly assessed by the ground reaction force in relation to the knee joint center and the shank-to-vertical angle, is crucial for their effectiveness but is time-consuming. This study validates automated pose estimator DeepLabCut on measuring these metrics using 2D videos with force vector overlay in individuals with neurological disorders wearing ankle-foot orthoses. MethodsThirty subjects with neurological disorders wearing ankle-foot orthoses participated. DeepLabCut's performance was compared to 3D gait analysis (Vicon). Subjects were randomly divided into three groups to train (20 subjects) and evaluate (10 subjects) DeepLabCut models. The number of subjects with untrackable points of interests in their videos was determined. The mean difference, root mean square error, intraclass correlation and repeatability coefficient between DeepLabCut and Vicon were computed for videos with trackable points of interest. FindingsOnly two subjects for ground reaction force distance to the knee and four subjects for the shank-to-vertical angle had untrackable points of interest. Excellent agreement between DeepLabCut and Vicon was found for the ground reaction force distance to the knee (mean difference 0.1 mm, root mean square error 6.7 mm, intraclass correlation 0.99, repeatability coefficient 6.7 mm) and shank-to-vertical angle (mean difference 0.7°, root mean square error 1.8°, intraclass correlation 0.99, repeatability coefficient 1.6°). InterpretationThis study demonstrates that DeepLabCut can accurately and efficiently assess ankle-foot orthosis alignment parameters, paving the way for a simple and straightforward 2D video setup with a force-vector overlay for automated ankle-foot orthosis alignment.

LabEmbryoCam: An opensource phenotyping system for developing aquatic animals

Phenomics is the acquisition of high-dimensional data on an individual-wide scale and is proving transformational in areas of biological research related to human health including medicine and the crop sciences. However, more broadly, a lack of accessible transferrable technologies and research approaches is significantly hindering the uptake of phenomics, in contrast to molecular-omics for which transferrable technologies have been a significant enabler. Aquatic embryos are natural models for phenomics, due to their small size, taxonomic diversity, ecological relevance, and high levels of temporal, spatial and functional change. Here, we present LabEmbryoCam, an autonomous phenotyping platform for timelapse imaging of developing aquatic embryos cultured in a multiwell plate format, and while optimised for embryos, the instrument is extremely versatile. The LabEmbryoCam capitalises on 3D printing, single board computers, consumer electronics and stepper motor enabled motion. These provide autonomous X, Y and Z motion, a web application streamlined for rapid setup of experiments, user email notifications and a humidification chamber to reduce evaporation over prolonged acquisitions. Downstream analyses are provided, enabling automated embryo segmentation, heartbeat detection, motion tracking, and energy proxy trait (EPT) measurement. LabEmbryoCam is a scalable, and flexible laboratory instrument, that leverages embryonic and early life stage organisms to tackle key global challenges including biological sensitivity assessment, toxicological screening, but also to support broader engagement with the earliest stages of life.

Remote Extended Reality With Markerless Motion Tracking for Sitting Posture Training

Remote Extended Reality With Markerless Motion Tracking for Sitting Posture Training