Efficient markerless genetic manipulation of Pasteurella multocida using lacZ and pheSm as selection markers.

Pasteurella multocida is a zoonotic conditional pathogen that infects multiple livestock species, causing substantial economic losses in the animal husbandry industry. An efficient markerless method for gene manipulation may facilitate the investigations of P. multocida gene function and pathogenesis of P. multocida. Herein, a temperature-sensitive shuttle vector was constructed using lacZ as a selection marker, and markerless glgB, opa, and hyaE mutants of P. multocida were subsequently constructed through blue-white colony screening. The screening efficiency of markerless deletion strains was improved by the lacZ system, and the method could be used for multiple gene deletions. However, the fur mutant was unavailable via this method. Therefore, we constructed a pheSm screening system based on mutated phenylalanine tRNA synthetase as a counterselection marker to achieve fur deletion mutant. The transformed strain was sensitive to 20 mM p-chloro-phenylalanine, demonstrating the feasibility of pheSm as a counter-selective marker. The pheSm system was used for markerless deletions of glgB, opa, and hyaE as well as fur that could not be screened by the lacZ system. A comparison of screening efficiencies of the system showed that the pheSm counterselection system was more efficient than the lacZ system and broadly applicable for mutant screening. The methods developed herein may provide valuable tools for genetic manipulation of P. multocida.IMPORTANCEPasteurella multocida is a highly contagious zoonotic pathogen. An understanding of its underlying pathogenic mechanisms is of considerable importance and requires efficient species-specific genetic tools. Herein, we propose a screening system for P. multocida mutants using lacZ or pheSm screening markers. We evaluated the efficiencies of both systems, which were used to achieve markerless deletion of multiple genes. The results of this study support the use of lacZ or pheSm as counterselection markers to improve counterselection efficiency in P. multocida. This study provides an effective genetic tool for investigations of the virulence gene functions and pathogenic mechanisms of P. multocida.

Expanding the genetic toolbox for Neisseria meningitidis with efficient tools for unmarked gene editing, complementation, and labeling.

The efficient natural transformation of Neisseria meningitidis allows the rapid construction of bacterial mutants in which the genes of interest are interrupted or replaced by antibiotic-resistance cassettes. However, this proved to be a double-edged sword, i.e., although facilitating the genetic characterization of this important human pathogen, it has limited the development of strategies for constructing markerless mutants without antibiotic-resistance markers. In addition, efficient tools for complementation or labeling are also lacking in N. meningitidis. In this study, we significantly expand the meningococcal genetic toolbox by developing new and efficient tools for the construction of markerless mutants (using a dual counterselection strategy), genetic complementation (using integrative vectors), and cell labeling (using a self-labeling protein tag). This expanded toolbox paves the way for more in-depth genetic characterization of N. meningitidis and might also be useful in other Neisseria species.IMPORTANCENeisseria meningitidis and Neisseria gonorrhoeae are two important human pathogens. Research focusing on these bacteria requires genetic engineering, which is facilitated by their natural ability to undergo transformation. However, the ease of mutant engineering has led the Neisseria community to neglect the development of more sophisticated tools for gene editing, particularly for N. meningitidis. In this study, we have significantly expanded the meningococcal genetic toolbox by developing novel and efficient tools for markerless mutant construction, genetic complementation, and cell tagging. This expanded toolbox paves the way for more in-depth genetic characterization of N. meningitidis and might also be useful in other Neisseria species.

Development of a thermostable Cre/lox-based gene disruption system and in vivo manipulations of the megaplasmid pTT27 in Thermus thermophilus HB27.

We previously reported the development of a Cre/lox-based gene disruption system for multiple markerless gene disruption in Thermus thermophilus; however, it was a time-consuming method because it functioned at 50 °C, the minimum growth temperature of T. thermophilus HB27. In the present study, we improved this system by introducing random mutations into the cre-expressing plasmid, pSH-Cre. One of the resulting mutant plasmids, pSH-CreFM allowed us to remove selection marker genes by Cre-mediated recombination at temperatures up to 70 °C. By using the thermostable Cre/lox system with pSH-CreFM, we successfully constructed two valuable pTT27 megaplasmid mutant strains, a plasmid-free strain and ß-galactosidase gene deletion strain, which were produced by different methods. The thermostable Cre/lox system improved the time-consuming nature of the original Cre/lox system, but it was not suitable for multiple markerless gene disruption in T. thermophilus because of its highly efficient induction of Cre-mediated recombination even at 70 °C. However, in vivo megaplasmid manipulations performed at 65 °C were faster and easier than with the original Cre/lox system. Collectively, these results indicate that this system is a powerful tool for engineering T. thermophilus megaplasmids.

Pioneering precision in markerless strain development for Synechococcus sp. PCC 7002.

Marine cyanobacteria such as Picosynechococcus sp. (formerly called Synechococcus sp.) PCC 7002 are promising chassis for photosynthetic production of commodity chemicals with low environmental burdens. Genetic engineering of cyanobacteria conventionally employs antibiotic resistance markers. However, limited availability of antibiotic-resistant markers is a problem for highly multigenic strain engineering. Although several markerless genetic manipulation methods have been developed for PCC 7002, they often lack versatility due to the requirement of gene disruption in the host strain. To achieve markerless transformation in Synechococcus sp. with no requirements for the host strain, this study developed a method in which temporarily introduces a mutated phenylalanyl-tRNA synthetase gene (pheS) into the genome for counter selection. Amino acid substitutions in the PheS that cause high susceptibility of PCC 7002 to the phenylalanine analog p-chlorophenylalanine were examined, and the combination of T261A and A303G was determined as the most suitable mutation. The mutated PheS-based selection was utilized for the markerless knockout of the nblA gene in PCC 7002. In addition, the genetic construct containing the lldD and lldP genes from Escherichia coli was introduced into the ldhA gene site using the counter selection strategy, resulting in a markerless recombinant strain. The repeatability of this method was demonstrated by the double markerless knockin recombinant strain, suggesting it will be a powerful tool for multigenic strain engineering of cyanobacteria.

Joint angle estimation during shoulder abduction exercise using contactless technology.

BACKGROUND: Tele-rehabilitation, also known as tele-rehab, uses communication technologies to provide rehabilitation services from a distance. The COVID-19 pandemic has highlighted the importance of tele-rehab, where the in-person visits declined and the demand for remote healthcare rises. Tele-rehab offers enhanced accessibility, convenience, cost-effectiveness, flexibility, care quality, continuity, and communication. However, the current systems are often not able to perform a comprehensive movement analysis. To address this, we propose and validate a novel approach using depth technology and skeleton tracking algorithms. METHODS: Our data involved 14 participants (8 females, 6 males) performing shoulder abduction exercises. We collected depth videos from an LiDAR camera and motion data from a Motion Capture (Mocap) system as our ground truth. The data were collected at distances of 2 m, 2.5 m, and 3.5 m from the LiDAR sensor for both arms. Our innovative approach integrates LiDAR with the Cubemos and Mediapipe skeleton tracking frameworks, enabling the assessment of 3D joint angles. We validated the system by comparing the estimated joint angles versus Mocap outputs. Personalized calibration was applied using various regression models to enhance the accuracy of the joint angle calculations. RESULTS: The Cubemos skeleton tracking system outperformed Mediapipe in joint angle estimation with higher accuracy and fewer errors. The proposed system showed a strong correlation with Mocap results, although some deviations were present due to noise. Precision decreased as the distance from the camera increased. Calibration significantly improved performance. Linear regression models consistently outperformed nonlinear models, especially at shorter distances. CONCLUSION: This study showcases the potential of a marker-less system, to proficiently track body joints and upper-limb angles. Signals from the proposed system and the Mocap system exhibited robust correlation, with Mean Absolute Errors (MAEs) consistently below [Formula: see text]. LiDAR's depth feature enabled accurate computation of in-depth angles beyond the reach of traditional RGB cameras. Altogether, this emphasizes the depth-based system's potential for precise joint tracking and angle calculation in tele-rehab applications.

Validity and Reliability of Upper Limb Kinematic Assessment Using a Markerless Motion Capture (MMC) System: A Pilot Study.

OBJECTIVE: To investigate the validity and test-retest reliability of a customized markerless motion capture (MMC) system that used iPad Pros with a Light Detection And Ranging scanner at two different viewing angles to measure the active range of motion (AROM) and the angular waveform of the upper-limb-joint angles of healthy adults performing functional tasks. DESIGN: Participants were asked to perform shoulder and elbow actions for the investigator to take AROM measurements, followed by four tasks that simulated daily functioning. Each participant attended 2 experimental sessions, which were held at least 2 days and at most 14 days apart. SETTING: A Vicon system and 2 iPad Pros installed with our MMC system were placed at 2 different angles to the participants and recorded their movements concurrently during each task. PARTICIPANTS: Thirty healthy adults (mean age: 28.9, M/F ratio: 40/60). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The AROM and the angular waveform of the upper-limb-joint angles. RESULTS: The iPad Pro MMC system underestimated the shoulder joint and elbow joint angles in all four simulated functional tasks. The MMC demonstrated good to excellent test-retest reliability for the shoulder joint AROM measurements in all 4 tasks. CONCLUSIONS: The maximal AROM measurements calculated by the MMC system had consistently smaller values than those measured by the goniometer. An MMC in iPad Pro system might not be able to replace conventional goniometry for clinical ROM measurements, but it is still suggested for use in home-based and telerehabilitation training for intra-subject measurements because of its good reliability, low cost, and portability. Further development to improve its performance in motion capture and analysis in disease populations is warranted.

Feasibility and usefulness of video-based markerless two-dimensional automated gait analysis, in providing objective quantification of gait and complementing the evaluation of gait in children with cerebral palsy.

BACKGROUND: Gait analysis aids in evaluation, classification, and follow-up of gait pattern over time in children with cerebral palsy (CP). The analysis of sagittal plane joint kinematics is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Is video-based 2-dimensional markerless (2D ML) gait analysis with automated processing a feasible and useful tool to quantify deviations, evaluate and classify gait, in children with CP? METHODS: Twenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I-III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate sagittal plane hip, knee, ankle kinematics, foot orientation and spatio-temporal parameters including gait speed and step length. RESULTS: Mean age was 10.4 (range 6.8-16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup of the measurement system took 15 min, acquisition 5-15 min and processing 50 min per child. Using the 2D ML method kinematic deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001. CONCLUSION: 2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.

Exploring stroke survivors' and physiotherapists' perspectives of the potential for markerless motion capture technology in community rehabilitation.

INTRODUCTION: Many stroke survivors do not receive optimal levels of personalised therapy to support their recovery. Use of technology for stroke rehabilitation has increased in recent years to help minimise gaps in service provision. Markerless motion capture technology is currently being used for musculoskeletal and occupational health screening and could offer a means to provide personalised guidance to stroke survivors struggling to access rehabilitation. AIMS: This study considered context, stakeholders, and key uncertainties surrounding the use of markerless motion capture technology in community stroke rehabilitation from the perspectives of stroke survivors and physiotherapists with a view to adapting an existing intervention in a new context. METHODS: Three focus groups were conducted with eight stroke survivors and five therapists. Data were analysed using reflexive thematic analysis. RESULTS: Five themes were identified: limited access to community care; personal motivation; pandemic changed rehabilitation practice; perceptions of technology; and role of markerless technology for providing feedback. CONCLUSIONS: Participants identified problems associated with the access of community stroke rehabilitation, exacerbated by Covid-19 restrictions. Participants were positive about the potential for the use of markerless motion capture technology to support personalised, effective stroke rehabilitation in the future, providing it is developed to meet stroke survivor specific needs.

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets.

PURPOSE: To assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT. METHODS: Diaphragm as surrogate for markers: a dome-shaped phantom with implanted markers was fabricated and underwent dual-orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB-approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in-house program to back-project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom "diaphragm" was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath-hold-exhale datasets. The MDTT workflow was applied until just prior to MV beam-on. RESULTS: Diaphragm as surrogate for markers: phantom data confirmed the in-house 3D back-projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean ± RMS difference of 0.70 ± 0.89, 1.08 ± 1.26, and 0.96 ± 1.06 mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory-correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean ± SD difference between the image-detected and predicted "diaphragm" position was 0.52 ± 0.18 mm. The 2D film gamma (2%/2 mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image-detected and predicted diaphragm position was 2.02 ± 0.92 mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7 mm in the ML, SI, and AP directions. CONCLUSION: In phantom/patients, the diaphragm motion correlated well with markers' motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.

Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an LSTM autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications.

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human-robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.

A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications?

In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose estimation models usually only provide joint centers, which are incomplete data for calculating joint angles in all anatomical axes. Recently, marker augmentation models based on deep learning have emerged. These models transform pose estimation data into complete anatomical data. Building on this concept, this study presents three marker augmentation models of varying complexity that were compared to a photogrammetry system. The errors in anatomical landmark positions and the derived joint angles were calculated, and a statistical analysis of the errors was performed to identify the factors that most influence their magnitude. The proposed Transformer model improved upon the errors reported in the literature, yielding position errors of less than 1.5 cm for anatomical landmarks and 4.4 degrees for all seven movements evaluated. Anthropometric data did not influence the errors, while anatomical landmarks and movement influenced position errors, and model, rotation axis, and movement influenced joint angle errors.

Submillimeter-Accurate Markerless Hand-Eye Calibration Based on a Robot's Flange Features.

An accurate and reliable estimation of the transformation matrix between an optical sensor and a robot is a key aspect of the hand-eye system calibration process in vision-guided robotic applications. This paper presents a novel approach to markerless hand-eye calibration that achieves streamlined, flexible, and highly accurate results, even without error compensation. The calibration procedure is mainly based on using the robot's tool center point (TCP) as the reference point. The TCP coordinate estimation is based on the robot's flange point cloud, considering its geometrical features. A mathematical model streamlining the conventional marker-based hand-eye calibration is derived. Furthermore, a novel algorithm for the automatic estimation of the flange's geometric features from its point cloud, based on a 3D circle fitting, the least square method, and a nearest neighbor (NN) approach, is proposed. The accuracy of the proposed algorithm is validated using a calibration setting ring as the ground truth. Furthermore, to establish the minimal required number and configuration of calibration points, the impact of the number and the selection of the unique robot's flange positions on the calibration accuracy is investigated and validated by real-world experiments. Our experimental findings strongly indicate that our hand-eye system, employing the proposed algorithm, enables the estimation of the transformation between the robot and the 3D scanner with submillimeter accuracy, even when using the minimum of four non-coplanar points for calibration. Our approach improves the calibration accuracy by approximately four times compared to the state of the art, while eliminating the need for error compensation. Moreover, our calibration approach reduces the required number of the robot's flange positions by approximately 40%, and even more if the calibration procedure utilizes just four properly selected flange positions. The presented findings introduce a more efficient hand-eye calibration procedure, offering a superior simplicity of implementation and increased precision in various robotic applications.

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis.

(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.

Design of Virtual Hands for Natural Interaction in the Metaverse.

The emergence of the Metaverse is raising important questions in the field of human-machine interaction that must be addressed for a successful implementation of the new paradigm. Therefore, the exploration and integration of both technology and human interaction within this new framework are needed. This paper describes an innovative and technically viable proposal for virtual shopping in the fashion field. Virtual hands directly scanned from the real world have been integrated, after a retopology process, in a virtual environment created for the Metaverse, and have been integrated with digital nails. Human interaction with the Metaverse has been carried out through the acquisition of the real posture of the user's hands using an infrared-based sensor and mapping it in its virtualized version, achieving natural identification. The technique has been successfully tested in an immersive shopping experience with the Meta Quest 2 headset as a pilot project, where a transactions mechanism based on the blockchain technology (non-fungible tokens, NFTs) has allowed for the development of a feasible solution for massive audiences. The consumers' reactions were extremely positive, with a total of 250 in-person participants and 120 remote accesses to the Metaverse. Very interesting technical guidelines are raised in this project, the resolution of which may be useful for future implementations.

Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait.

Pathological gait in patients with Hakim's disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson's disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. We analyzed 1491 measurements of 1 m diameter circular walking from 122, 12, and 93 patients with HD, PD, and CM, respectively, and 200 healthy volunteers using the Three-Dimensional Pose Tracker for Gait Test. Upper and lower limb movements of 2D coordinates projected onto body axis sections were derived from estimated 3D relative coordinates. The hip and knee joint angle ranges on the sagittal plane were significantly smaller in the following order: healthy > CM > PD > HD, whereas the shoulder and elbow joint angle ranges were significantly smaller, as follows: healthy > CM > HD > PD. The outward shift of the leg on the axial plane was significantly greater, as follows: healthy < CM < PD < HD, whereas the outward shift of the upper limb followed the order of healthy > CM > HD > PD. The strongest correlation between the upper and lower limb movements was identified in the angle ranges of the hip and elbow joints on the sagittal plane. The lower and upper limb movements during circular walking were correlated. Patients with HD and PD exhibited reduced back-and-forth swings of the upper and lower limbs.

A Step Forward Understanding Directional Limitations in Markerless Smartphone-Based Gait Analysis: A Pilot Study.

The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system-Vicon. Our focus was on its performance in capturing walking both toward and away from two iPhone cameras in the same setting, which allowed capturing the Timed Up and Go (TUG) test. The performance of the OpenCap system was compared to that of a standard marker-based system by comparing spatial-temporal and kinematic parameters in 10 participants. The study focused on identifying potential discrepancies in accuracy and comparing results using correlation analysis. Case examples further explored our results. The OpenCap system demonstrated good accuracy in spatial-temporal parameters but faced challenges in accurately capturing kinematic parameters, especially in the walking direction facing away from the cameras. Notably, the two walking directions observed significant differences in pelvic obliquity, hip abduction, and ankle flexion. Our findings suggest areas for improvement in markerless technologies, highlighting their potential in clinical settings.

Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson's Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems.

BACKGROUND: This study tested the agreement between a markerless motion capture system and force-plate system ("gold standard") to quantify stability control and motor performance during gait initiation. METHODS: Healthy adults (young and elderly) and patients with Parkinson's disease performed gait initiation series at spontaneous and maximal velocity on a system of two force-plates placed in series while being filmed by a markerless motion capture system. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of motor performance) and the braking index (indicator of stability control). RESULTS: Descriptive statistics indicated that both systems detected between-group differences and velocity effects similarly, while a Bland-Altman plot analysis showed that mean biases of both biomechanical indicators were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (braking index) and moderate (motor performance) evidence that both systems provided equivalent values. However, a trial-by-trial analysis of Bland-Altman plots revealed the possibility of differences >10% between the two systems. CONCLUSION: Although non-negligible differences do occur, a markerless motion capture system appears to be as efficient as a force-plate system in detecting Parkinson's disease and velocity condition effects on the braking index and motor performance.

Evaluation of the Margin of Stability during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults and Patients with Parkinson's Disease: A Comparison of Force Plate and Markerless Motion Capture Systems.

Gait initiation (GI) is a functional task classically used in the literature to evaluate the capacity of individuals to maintain postural stability. Postural stability during GI can be evaluated through the "margin of stability" (MoS), a variable that is often computed from force plate recordings. The markerless motion capture system (MLS) is a recent innovative technology based on deep learning that has the potential to compute the MoS. This study tested the agreement between a force plate measurement system (FPS, gold standard) and an MLS to compute the MoS during GI. Healthy adults (young [YH] and elderly [EH]) and Parkinson's disease patients (PD) performed GI series at spontaneous (SVC) and maximum velocity (MVC) on an FPS while being filmed by a MLS. Descriptive statistics revealed a significant effect of the group (YH vs. EH vs. PD) and velocity condition (SVC vs. MVC) on the MoS but failed to reveal any significant effect of the system (MLS vs. PFS) or interaction between factors. Bland-Altman plot analysis further showed that mean MoS biases were zero in all groups and velocity conditions, while the Bayes factor 01 indicated "moderate evidence" that both systems provided equivalent MoS. Trial-by-trial analysis of Bland-Altman plots, however, revealed that differences of >20% between the two systems did occur. Globally taken, these findings suggest that the two systems are similarly effective in detecting an effect of the group and velocity on the MoS. These findings may have important implications in both clinical and laboratory settings due to the ease of use of the MLS compared to the FPS.