Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip-knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Experimental Comparison between 4D Stereophotogrammetry and Inertial Measurement Unit Systems for Gait Spatiotemporal Parameters and Joint Kinematics.

(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with an inertial motion capture system (Xsens) to evaluate their reliability and accuracy in assessing gait spatiotemporal parameters and joint kinematics. (2) Methods: This study included 13 healthy people and one hemiplegic patient, and it examined stance time, swing time, cycle time, and stride length. Statistical analysis included paired samples t-test, Bland-Altman plot, and the intraclass correlation coefficient (ICC). (3) Results: A high degree of agreement and no significant difference (p > 0.05) between the two measurement systems have been found for stance time, swing time, and cycle time. Evaluation of stride length shows a significant difference (p < 0.05) between Xsens and Move4D. The highest root-mean-square error (RMSE) was found in hip flexion/extension (RMSE = 10.99°); (4) Conclusions: The present work demonstrated that the system Move4D can estimate gait spatiotemporal parameters (gait phases duration and cycle time) and joint angles with reliability and accuracy comparable to Xsens. This study allows further innovative research using 4D (3D over time) scanning for quantitative gait assessment in clinical practice.

A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway.

Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.

[Gait Analysis in Patients with Adolescent Idiopathic Scoliosis]. / Analýza chuze u pacientu s adolescentní idiopatickou skoliózou.

PURPOSE OF THE STUDY: The study describes changes in gait parameters (temporal-spatial parameters, kinematic parameters represented by the global Gait Deviation Index) of individuals with Adolescent Idiopathic Scoliosis (AIS) compared to the healthy population. The hypothesis assumed a difference in the observed parameters between the two mentioned groups. MATERIAL AND METHODS: In a retrospective study, the temporal-spatial parameters and Gait Deviation Index (GDI) of a cohort of 45 AIS patients (36 girls and 9 boys with the mean age of 15.2 years, the mean Cobb angle of the thoracic curve of 47.3° and the lumbar curve of 51.8°) were compared to a typically developing population of 12 healthy individuals with no musculoskeletal pathology. The difference of followed-up parameters in patients with AIS compared to normal values was assessed by one-sample Student's T-test at the significance level of p = 0.05. RESULTS: The gait analysis shows significant deviations in the gait stereotype of patients with AIS compared to the healthy population. Statistically significant differences within temporal-spatial parameters were confirmed for cadence, walking speed, step time, stride time for left leg, step length, stride length and step width. The mean GDI of the cohort reached the value of 91.07 that indicates a slight alteration of gait, however, even this change is statistically significant. DISCUSSION: In our cohort of patients with AIS, we identified a significantly reduced walking speed (on average 15.4% compared to normal values. At the same time, a reduction in cadence (by an average of 7.5%) and an increase of the stride time (by an average of 12%) were recorded. Our mean GDI values were 91.07, which is consistent with the results reported in the literature for comparable groups of AIS patients. CONCLUSIONS: Our study demonstrated that AIS significantly affects gait stereotype. The differences compared to the group of healthy individuals within temporal-spatial parameters were confirmed for cadence, walking speed, duration and length of step and stride, and step width. The kinematic analysis of gait using the global (GDI) index in patients with AIS demonstrated its slight alteration. A better understanding of the change in movement stereotypes and gait in patients with AIS can bring wider possibilities for individualizing conservative treatment and also can help prevent secondary changes in the locomotor system. KEY WORDS: adolescent idiopathic scoliosis, AIS, gait analysis, Gait Deviation Index, GDI.

Extraction and Validation of Biomechanical Gait Parameters with Contactless FMCW Radar.

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range-velocity-time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland-Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.

Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis.

BACKGROUND: Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods. OBJECTIVE: This study systematically analyzed the steps required to automate the video analysis workflow by investigating the applicability of a threshold-based event detection algorithm developed for 3D marker trajectories to 2D video data at four sampling rates; the agreement of 2D keypoints estimated by an off-the-shelf pose estimation model compared with gold-standard 3D marker trajectories projected to camera's field of view; and the influence of an offset in event detection on contact time and the sagittal knee joint angle at the key critical events of touch down and foot flat. METHODS: Repeated measures limits of agreement were used to compare parameters determined by markerless and marker-based motion capture. RESULTS: Results highlighted that a minimum video sampling rate of 100 Hz is required to detect key events, and the limited applicability of 3D marker trajectory-based event detection algorithms when using 2D video. Although detected keypoints showed good agreement with the gold-standard, misidentification of key events-such as touch down by 20 ms resulted in knee compression angle differences of up to 20°. CONCLUSION: These findings emphasize the need for de novo accurate key event detection algorithms to automate 2D video analysis pipelines.

Prevalence of movement asymmetries in high-performing riding horses perceived as free from lameness and riders' perception of horse sidedness.

A high proportion of horses in training, perceived as free from lameness by their owner, exhibit vertical movement asymmetries. These types of asymmetries are sensitive measures of lameness, but their specificity as indicators of orthopaedic pathology or locomotor function remains unclear. Equine athletes performing at a high level could be assumed to exhibit a higher degree of movement symmetry compared with the general horse population, but this has not been confirmed. This study investigated the prevalence of movement asymmetries in horses performing at a high level in three equestrian disciplines; show jumping, dressage and eventing, as well as the association between riders' perception of horse sidedness and said movement asymmetries. Using an inertial measurement unit-based system (Equinosis), gait analysis was performed on 123 high-performing horses. The mean difference between the two vertical minimum and between the two maximum values of each stride was recorded for the head (HDmin, HDmax) and pelvis (PDmin, PDmax). The horses were defined as asymmetric if one or multiple asymmetry parameters exceeded an absolute trial mean of: >6mm for HDmin or HDmax, and >3mm for PDmin or PDmax, with standard deviation less than the respective mean value. Based on the results, 70% of the horses were classified as asymmetric, which is similar to previous findings for young riding horses and horses competing at a lower level. More than one-third of these high-performing horses had asymmetry values of similar magnitude to those seen in clinically lame horses. No clear associations were observed between rider-perceived sidedness and the vertical movement asymmetry values, indicating that the perceived unevenness between sides is not a determinant of vertical movement asymmetry. Longitudinal studies on movement asymmetries in relation to training intensity and full clinical examinations with local or systemic analgesic testing are desired as further research to determine whether these movement asymmetries indicate a welfare problem.

Relevance of instrumented gait analysis in the prediction of the rebound phenomenon after guided growth intervention.

Predictors of rebound after correction of coronal plane deformities using temporary hemiepiphysiodesis (TH) are not well defined. The following research questions were tested: (1) Is the dynamic knee joint load useful to improve rebound prediction accuracy? (2) Does a large initial deformity play a critical role in rebound development? (3) Are BMI and a young age risk factors for rebound? Fifty children and adolescents with idiopathic knee valgus malalignment were included. A deviation of the mechanical femorotibial angle (MFA) of ≥ 3° into valgus between explantation and the one-year follow-up period was chosen to classify a rebound. A rebound was detected in 22 of the 50 patients (44%). Two predictors of rebound were identified: 1. reduced peak lateral knee joint contact force in the first half of the stance phase at the time of explantation (72.7% prediction); 2. minor initial deformity according to the MFA (70.5% prediction). The best prediction (75%) was obtained by including both parameters in the binary logistic regression method. A TH should not be advised in patients with a minor initial deformity of the leg axis. Dynamic knee joint loading using gait analysis and musculoskeletal modeling can be used to determine the optimum time to remove the plates.

Clinical Feasibility of a Markerless Gait Analysis System.

Background: The gait analysis method that has been used in clinical practice to date is an optical tracking system (OTS) using a marker, but a markerless gait analysis (MGA) system is being developed because of the expensive cost and complicated examination of the OTS. To apply this MGA clinically, a comparative study of the MGA and OTS methods is necessary. The purpose of this study was to evaluate the compatibility between the OTS and the MGA methods and to evaluate the usefulness of the MGA system in actual clinical settings. Methods: From March 2021 to August 2021, 14 patients underwent gait analysis using the OTS and MGA system, and the spatiotemporal parameters and kinematic results obtained by the 2 methods were compared. To evaluate the practicality of the MGA system in an actual clinical setting, MGA was performed on 14 symptomatic children with idiopathic toe walking, who had been treated with a corrective cast, and the pre-cast and post-cast results were compared. For the OTS, the Motion Analysis Eagle system was used, and for MGA, DH Walk was used. Results: The spatiotemporal parameters showed no significant difference between the OTS and MGA system. The joint angle graphs of the kinematics along the sagittal plane showed similar shapes as a whole, with particularly high correlations in the hip and knee (pelvis: 29.4%, hip joint: 96.7%, knee joint: 94.9%, and ankle joint: 68.5%). A quantified comparison using the CORrelation and Analysis (CORA) score also showed high similarity between the 2 methods. The MGA results of pre-cast application and post-cast removal for children with idiopathic toe walking showed a statistically significant improvement in ankle dorsiflexion after treatment (p < 0.001). Conclusions: MGA showed a good correlation with the conventional OTS in terms of spatiotemporal parameters and kinematics. We demonstrated that ankle sagittal kinematics improved after treatment by corrective cast in children with idiopathic toe walking using the MGA method. Thus, after the improvement of a few limitations, the MGA system may soon be able to be clinically applied.

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study.

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy.

[An update on clinical gait analysis : Current developments and applications]. / Update klinische Ganganalyse : Aktuelle Entwicklungen und Einsatzmöglichkeiten.

The objective acquisition and assessment of joint movements and loads using instrumented gait analysis has become an established tool in clinical diagnostics. In particular, marker-based 3D gait analyses make use of an increasingly comprehensive database for the assessment of orthopaedic or neurological questions. Based on this data and medical-scientific experience, increasingly reliable approaches and evaluation strategies are emerging, which also draw on methods from artificial intelligence and musculoskeletal modelling. This article focusses on marker-based gait analyses of the lower extremity (hip, knee, foot) and how these can be used in a clinically relevant way using current methods, e.g. for determining indications or optimization of surgical planning. Finally, current developments and applications by using alternative methods from sensor technology and optical motion capture will be briefly discussed.

Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation.

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review.

Background: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

The efficacy of 3D gait analysis to evaluate surgical (and rehabilitation) outcome after degenerative lumbar surgery.

BACKGROUND: Lumbar degenerative conditions are a major cause of back pain and disability in individuals aged 45 and above. Gait analysis utilizes sensor technology to collect movement data, aiding in the evaluation of various gait aspects like spatiotemporal parameters, joint angles, neuromuscular activity, and joint forces. It is widely used in conditions such as cerebral palsy and knee osteoarthritis. This research aims to assess the effectiveness of 3D gait analysis in evaluating surgical outcomes and postoperative rehabilitation for lumbar degenerative disorders. METHODS: A prospective self-controlled before-after study (n = 85) carried out at our Hospital (Sep 2018 - Dec 2021) utilized a 3D motion analysis system to analyze gait in patients with lumbar degenerative diseases. The study focused on the multifidus muscle, a crucial spinal muscle, during a minimally invasive lumbar interbody fusion surgery conducted by Shandong Weigao Pharmaceutical Co., Ltd. Pre- and postoperative assessments included time-distance parameters (gait speed, stride frequency, stride length, stance phase), hip flexion angle, and stride angle. Changes in 3D gait parameters post-surgery and during rehabilitation were examined. Pearson correlation coefficient was employed to assess relationships with the visual analog pain scale (VAS), Oswestry Disability Index (ODI), and Japanese Orthopedic Association (JOA) scores. Patient sagittal alignment was evaluated using "Surgimap" software from two types of lateral radiographs to obtain parameters like pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), lumbar lordosis (LL), intervertebral space height (DH), posterior height of the intervertebral space (PDH) at the operative segment, and anterior height of the intervertebral space (ADH). RESULTS: By the 6th week post-operation, significant improvements were observed in the VAS score, JOA score, and ODI score of the patients compared to preoperative values (P < 0.05), along with notable enhancements in 3D gait quantification parameters (P < 0.05). Pearson correlation analysis revealed a significant positive correlation between improvements in 3D gait quantification parameters and VAS score, JOA score, and ODI value (all P < 0.001). CONCLUSION: 3D gait analysis is a valuable tool for evaluating the efficacy of surgery and rehabilitation training in patients.

Unobtrusive measurement of gait parameters using seismographs: An observational study.

Analyzing irregularities in walking patterns helps detect human locomotion abnormalities that can signal health changes. Traditional observation-based assessments have limitations due to subjective biases and capture only a single time point. Ambient and wearable sensor technologies allow continuous and objective locomotion monitoring but face challenges due to the need for specialized expertise and user compliance. This work proposes a seismograph-based algorithm for quantifying human gait, incorporating a step extraction algorithm derived from mathematical morphologies, with the goal of achieving the accuracy of clinical reference systems. To evaluate our method, we compared the gait parameters of 50 healthy participants, as recorded by seismographs, and those obtained from reference systems (a pressure-sensitive walkway and a camera system). Participants performed four walking tests, including traversing a walkway and completing the timed up-and-go (TUG) test. In our findings, we observed linear relationships with strong positive correlations (R2 > 0.9) and tight 95% confidence intervals for all gait parameters (step time, cycle time, ambulation time, and cadence). We demonstrated that clinical gait parameters and TUG mobility test timings can be accurately derived from seismographic signals, with our method exhibiting no significant differences from established clinical reference systems.

Estimation of minimum foot clearance using a single foot-mounted inertial sensor and personalized foot geometry scan.

The real-world measurement of minimum foot clearance (mFC) during the swing phase of gait is critical in efforts to understand and reduce the risk of trip-and-fall incidents in populations with gait impairments. Past research has focused on measuring clearance of a single point on a person's foot, typically the toe-however, this may overestimate mFC and may even be the wrong region of the foot in cases of gait impairments or interventions. In this work, we present a novel method to reconstruct the swing-phase trajectory of an arbitrary number of points on a person's shoe and estimate the instantaneous height and location of whole-foot mFC. This is achieved using a single foot-mounted inertial sensor and personalized shoe geometry scan, assuming a rigid-body IMU-shoe system. This combination allows collection and analysis using out-of-lab tests, potentially including clinical environments. Validation of single marker location using the proposed method vs. motion capture showed height errors with bias less than 0.05 mm, and 95% confidence interval of - 8.18 to + 8.09 mm. The method is demonstrated in an example data set comparing different interventions for foot drop, and it shows clear differences among no intervention, functional electrical stimulation, and ankle-foot orthosis conditions. This method offers researchers and clinicians a rich understanding of a person's gait by providing objective 3D foot kinematics and allowing a unique opportunity to view the regions of the foot where minimum clearance occurs. This information can contribute to a more informed recommendation of specific interventions or assistive technology than is currently possible in standard clinical practice.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

BACKGROUND: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Evaluation of forelimb gait variation overground at a walk in sound and lame dogs using a combination of diagnostic techniques.

BACKGROUND: Kinetic and kinematic gait analysis is increasingly practised as a part of lameness evaluation in dogs. The aim of this study was to examine the normal short- and long-term variation in forelimb gait in sound control dogs (CD) at a walk using seven selected variables of objective kinetic and kinematic gait analyses. Also, to compare the findings in CD to a group of forelimb lame dogs with elbow osteoarthritis (OAD). An additional aim was to test a kinetic based graphic method for lameness detection; symmetry squares (SS). A prospective longitudinal study was carried out on client owned CD and OAD. Clinical and orthopaedic evaluations were performed to ensure soundness and detect and grade lameness. Seven kinetic and kinematic variables and SS were tested for lameness evaluation. The CD were divided into two subgroups, CD1 and CD2, and examined twice: CD1 with two months interval and CD2 with 3-4 h interval. The OAD group was evaluated once and compared to the CD groups' first examination. RESULTS: Thirteen CD and 19 OAD were included. For CD1 and CD2, there were no significant differences in any examined variable between examination occasions. Total peak force/impulse symmetry and fore-hind peak force/impulse symmetry differed significantly between OAD and CD. Symmetry squares had a 74% agreement to subjective orthopaedic evaluations. CONCLUSIONS: In CD, no difference in the examined variables was seen between examination occasions. Four out of seven objective variables differed significantly between CD and OAD. The graphic SS method might have diagnostic potential for lameness detection, making it possible to detect a shift from lame to non-lame limbs. Potentially, this might be especially helpful in bilaterally lame dogs, which often represent a clinical challenge in lameness evaluation.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.