The impact of thigh and shank marker quantity on lower extremity kinematics using a constrained model

A Method to Track 3D Knee Kinematics by Multi-Channel 3D-Tracked A-Mode Ultrasound

This paper introduces a method for measuring 3D tibiofemoral kinematics using a multi-channel A-mode ultrasound system under dynamic conditions. The proposed system consists of a multi-channel A-mode ultrasound system integrated with a conventional motion capture system (i.e., optical tracking system). This approach allows for the non-invasive and non-radiative quantification of the tibiofemoral joint's six degrees of freedom (DOF). We demonstrated the feasibility and accuracy of this method in the cadaveric experiment. The knee joint's motions were mimicked by manually manipulating the leg through multiple motion cycles from flexion to extension. To measure it, six custom ultrasound holders, equipped with a total of 30 A-mode ultrasound transducers and 18 optical markers, were mounted on various anatomical regions of the lower extremity of the specimen. During experiments, 3D-tracked intra-cortical bone pins were inserted into the femur and tibia to measure the ground truth of tibiofemoral kinematics. The results were compared with the tibiofemoral kinematics derived from the proposed ultrasound system. The results showed an average rotational error of 1.51 ± 1.13° and a translational error of 3.14 ± 1.72 mm for the ultrasound-derived kinematics, compared to the ground truth. In conclusion, this multi-channel A-mode ultrasound system demonstrated a great potential of effectively measuring tibiofemoral kinematics during dynamic motions. Its improved accuracy, nature of non-invasiveness, and lack of radiation exposure make this method a promising alternative to incorporate into gait analysis and prosthetic kinematic measurements later.

3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units

Introduction: Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous pediatric population. This study aimed at 1) quantifying personalized and generalized ML models' performance for predicting gait time series in typically developed (TD) children using IMUs data, 2) Comparing random forest (RF) and convolutional neural networks (CNN) models' performance, 3) Finding the optimal number of IMUs required for accurate predictions. Methodology: Seventeen TD children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics (targets) were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized RF and CNN models. This procedure was initially conducted with 7 IMUs placed on all lower limb segments and then performed using only two IMUs on the feet. Results: Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. Furthermore, reducing the number of IMUs from 7 to 2 did not affect the results, and the performance remained consistent. Discussion: This study proposed a promising personalized approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.

Patellar Tendon Load Progression during Rehabilitation Exercises: Implications for the Treatment of Patellar Tendon Injuries

PURPOSE

This study aimed to evaluate patellar tendon loading profiles (loading index, based on loading peak, loading impulse, and loading rate) of rehabilitation exercises to develop clinical guidelines to incrementally increase the rate and magnitude of patellar tendon loading during rehabilitation.

METHODS

Twenty healthy adults (10 females/10 males, 25.9 ± 5.7 yr) performed 35 rehabilitation exercises, including different variations of squats, lunge, jumps, hops, landings, running, and sports specific tasks. Kinematic and kinetic data were collected, and a patellar tendon loading index was determined for each exercise using a weighted sum of loading peak, loading rate, and cumulative loading impulse. Then the exercises were ranked, according to the loading index, into tier 1 (loading index ≤0.33), tier 2 (0.33 < loading index <0.66), and tier 3 (loading index ≥0.66).

RESULTS

The single-leg decline squat showed the highest loading index (0.747). Other tier 3 exercises included single-leg forward hop (0.666), single-leg countermovement jump (0.711), and running cut (0.725). The Spanish squat was categorized as a tier 2 exercise (0.563), as was running (0.612), double-leg countermovement jump (0.610), single-leg drop vertical jump (0.599), single-leg full squat (0.580), double-leg drop vertical jump (0.563), lunge (0.471), double-leg full squat (0.428), single-leg 60° squat (0.411), and Bulgarian squat (0.406). Tier 1 exercises included 20 cm step up (0.187), 20 cm step down (0.288), 30 cm step up (0.321), and double-leg 60° squat (0.224).

CONCLUSIONS

Three patellar tendon loading tiers were established based on a combination of loading peak, loading impulse, and loading rate. Clinicians may use these loading tiers as a guide to progressively increase patellar tendon loading during the rehabilitation of patients with patellar tendon disorders and after anterior cruciate ligament reconstruction using the bone-patellar tendon-bone graft.

Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy study participants performing 8 daily living and exercise movements. We calculated the correlation (Rxy) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (Rxy ≥ 0.877, RMSD ≤ 5.9°) and moments (Rxy ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7-15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

Patellofemoral Joint Loading Progression Across 35 Weightbearing Rehabilitation Exercises and Activities of Daily Living

BACKGROUND

Exercises that provide progressive therapeutic loading are a central component of patellofemoral pain rehabilitation, but quantitative evidence on patellofemoral joint loading is scarce for a majority of common weightbearing rehabilitation exercises.

PURPOSE

To define a loading index to quantify, compare, rank, and categorize overall loading levels in the patellofemoral joint across 35 types of weightbearing rehabilitation exercises and activities of daily living.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Model-estimated knee flexion angles and extension moments based on motion capture and ground-reaction force data were used to quantify patellofemoral joint loading in 20 healthy participants who performed each exercise. A loading index was computed via a weighted sum of loading peak and cumulative loading impulse for each exercise. The 35 rehabilitation exercises and daily living activities were then ranked and categorized into low, moderate, and high "loading tiers" according to the loading index.

RESULTS

Overall patellofemoral loading levels varied substantially across the exercises and activities, with loading peak ranging from 0.6 times body weight during walking to 8.2 times body weight during single-leg decline squat. Most rehabilitation exercises generated a moderate level of patellofemoral joint loading. Few weightbearing exercises provided low-level loading that resembled walking or high-level loading with both high magnitude and duration. Exercises with high knee flexion tended to generate higher patellofemoral joint loading compared with high-intensity exercises.

CONCLUSION

This study quantified patellofemoral joint loading across a large collection of weightbearing exercises in the same cohort.

CLINICAL RELEVANCE

The visualized loading index ranks and modifiable worksheet may assist clinicians in planning patient-specific exercise programs for patellofemoral pain rehabilitation.

Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy subjects performing 8 daily living and exercise movements. We calculated the correlation (R xy ) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (R xy ≥ 0.877, RMSD ≤ 5.9°) and moments (R xy ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7° - 15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.

Effectiveness of Global Optimisation and Direct Kinematics in Predicting Surgical Outcome in Children with Cerebral Palsy

Multibody optimisation approaches have not seen much use in routine clinical applications despite evidence of improvements in modelling through a reduction in soft tissue artifacts compared to the standard gait analysis technique of direct kinematics. To inform clinical use, this study investigated the consistency with which both approaches predicted post-surgical outcomes, using changes in Gait Profile Score (GPS) when compared to a clinical assessment of outcome that did not include the 3D gait data. Retrospective three-dimensional motion capture data were utilised from 34 typically developing children and 26 children with cerebral palsy who underwent femoral derotation osteotomies as part of Single Event Multi-Level Surgeries. Results indicated that while, as expected, the GPS estimated from the two methods were numerically different, they were strongly correlated (Spearman's ρ = 0.93), and no significant differences were observed between their estimations of change in GPS after surgery. The two scores equivalently classified a worsening or improvement in the gait quality in 93% of the cases. When compared with the clinical classification of responders versus non-responders to the intervention, an equivalent performance was found for the two approaches, with 27/41 and 28/41 cases in agreement with the clinical judgement for multibody optimisation and direct kinematics, respectively. With this equivalent performance to the direct kinematics approach and the benefit of being less sensitive to skin artefact and allowing additional analysis such as estimation of musculotendon lengths and joint contact forces, multibody optimisation has the potential to improve the clinical decision-making process in children with cerebral palsy.

Experimental recommendations for estimating lower extremity loading based on joint and activity

Researchers often estimate joint loading using musculoskeletal models to solve the inverse dynamics problem. This approach is powerful because it can be done non-invasively, however, it relies on assumptions and physical measurements that are prone to measurement error. The purpose of this study was to determine the impact of these errors - specifically, segment mass and shear ground reaction force - have on analyzing joint loads during activities of daily living. We performed traditional marker-based motion capture analysis on 8 healthy adults while they completed a battery of exercises on 6 degree of freedom force plates. We then scaled the mass of each segment as well as the shear component of the ground reaction force in 5% increments between 0 and 200% and iteratively performed inverse dynamics calculations, resulting in 1681 mass-shear combinations per activity. We compared the peak joint moments of the ankle, knee, and hip at each mass-shear combination to the 100% mass and 100% shear combination to determine the percent error. We found that the ankle was most resistant to changes in both mass and shear and the knee was resistant to changes in mass while the hip was sensitive to changes in both mass and shear. These results can help guide researchers who are pursuing lower-cost or more convenient data collection setups.

Kinematic analysis of gait in an underwater treadmill using land-based Vicon T 40s motion capture cameras arranged externally

Aquatic therapy for rehabilitation can be performed in a variety of environments, which can vary from a traditional swimming pool to a self-contained underwater treadmill. While kinematic analysis has been performed in large volume swimming pools using specific underwater motion capture systems, researchers may only have access to a land-based motion-capture system, which is not waterproof. Additionally, underwater motion capture systems may not fit within the confines of a smaller underwater treadmill. Thus, the purpose of this study was to design and analyze methodology to quantify lower limb kinematics during an aquatic treadmill session, using a land-based motion capture system. Kinematics of lower limb motion at different speeds was studied while walking on an underwater treadmill in comparison to walking on the same treadmill without water (empty tank). The effects of the presence of water on walking kinematics was analyzed and interpreted using parametric and non-parametric testing procedures. The results suggest significant influences of speed on knee and ankle angles (p < 0.05) in both dryland and aquatic scenarios. Knee and ankle angle measures revealed no significant differences between the dryland and water treadmill scenarios (p > 0.05). The increased time requirement in water for the full gait cycle found in this study indicates influence of resistive effects. This finding can be especially suited for muscle strengthening and stabilizing treatments for lower limbs. Also, a framework was developed to realize a potential methodology to use land-based motion capture cameras to successfully analyze the kinematics of gait in constrained aquatic volumes.

Exercise Progression to Incrementally Load the Achilles Tendon

PURPOSE

The purposes of our study were to evaluate Achilles tendon loading profiles of various exercises and to develop guidelines to incrementally increase the rate and magnitude of Achilles tendon loading during rehabilitation.

METHODS

Eight healthy young adults completed a battery of rehabilitation exercises. During each exercise, we collected three-dimensional motion capture and ground reaction force data to estimate Achilles tendon loading biomechanics. Using these loading estimates, we developed an exercise progression that incrementally increases Achilles tendon loading based on the magnitude, duration, and rate of tendon loading.

RESULTS

We found that Achilles tendon loading could be incrementally increased using a set of either isolated ankle movements or multijoint movements. Peak Achilles tendon loads varied more than 12-fold, from 0.5 bodyweights during a seated heel raise to 7.3 bodyweights during a forward single-leg hop. Asymmetric stepping movements like lunges, step ups, and step downs provide additional flexibility for prescribing tendon loading on a side-specific manner.

CONCLUSION

By establishing progressions for Achilles tendon loading, rehabilitative care can be tailored to address the specific needs of each patient. Our comprehensive data set also provides clinicians and researchers guidelines on how to alter magnitude, duration, and rate of loading to design new exercises and exercise progressions based on the clinical need.

Simultaneous identification of human body model parameters and gait trajectory from 3D motion capture data

The analysis of human movements rests on a realistic human body model. Deducing model parameters from anthropomorphic data is challenging since these are inherently imprecise. An approach to improve model accuracy is the parameter adaptation based on motion data. 3D motion capture data are already being used for generating the trajectories of a human body model, so combining motion tracking and parameter identification seems most natural. This paper introduces a holistic approach to simultaneously identify the geometric parameters of a kinematic human lower limb model and the parameters defining a (cyclic) gait trajectory, based on 3D marker positions. The result is a time-continuous description of a physiologically compatible lower extremity movement along with optimal model parameters so to best reproduce the captured motion. The method takes into account restrictions such as the range of motion of human body joints and is robust against missing data due to marker occlusions or failures of the measurement system. Considering multiple gait cycles of a movement trial, we derive the characteristic motion pattern (CMP) of a specific subject walking at a specific speed. Our method further allows for motion analysis and assessment, but also for motion synthesis with arbitrary time span and time resolution and can thus be used for simulations and trajectory planning of rehabilitation and movement assistance systems, such as exoskeletons.

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.

A simple instrumented insole algorithm to estimate plantar flexion moments

BACKGROUND

Plantar flexion is critical for ambulatory function but there are few wearable solutions to monitor loading.

RESEARCH QUESTION

The purpose of this study was to develop and validate a method to calculate plantar flexion moment using a commercially-available instrumented insole.

METHODS

Seven healthy young adults completed a battery of functional activities to characterize a range of plantar flexion loading which included single leg heel raise, step down, and drop jump as well as walking and running at comfortable speeds. Lower extremity trajectories were captured using motion capture and ground reaction forces were recorded with embedded force plates as well as the instrumented insole. We compared plantar flexion moment calculated by the instrumented insole to 'gold standard' inverse dynamics.

RESULTS

We found that estimating plantar flexion moment using our instrumented insole algorithm compared favorably to moments calculated using inverse dynamics across all activities. Errors in the maximum plantar flexion moments were less than 10 % for all activities, averaging 4.9 %. Root mean square errors across the entire activity were also small, averaging 1.0 % bodyweight * height. Additionally, the calculated wave forms were strongly correlated with inverse dynamics (R_xy > 0.964).

SIGNIFICANCE

Our findings demonstrate the utility and fidelity of a simple method for estimating plantar flexion moment using a commercially available instrumented insole. By leveraging this simple methodology, it is now feasible to prospectively track and eventually prescribe plantar flexion loading outside of the clinic to improve patient outcomes.

Influence of musculotendon geometry variability in muscle forces and hip bone-on-bone forces during walking

Inverse dynamics problems are usually solved in the analysis of human gait to obtain reaction forces and moments at the joints. However, these actions are not the actual forces and moments supported by the joint structure, because they do not consider the forces of the muscles acting across the joint. Therefore, to analyse bone-on bone forces it is necessary to estimate those muscle forces. Usually, this problem is addressed by means of optimization algorithms. One of the parameters required to solve this problem is the musculotendon geometry. These data are usually taken from cadavers or MRI data from several subjects, different from the analysed subject. Then, the model is scaled to the subject morphology. This procedure constitutes a source of error. The goals of this work were two. First, to perform a sensitivity analysis of the influence of muscle insertion locations on the muscle forces acting on the hip joint and on the hip joint bone-on-bone forces. Second, to compare the hip joint bone-on-bone forces during gait cycle obtained through muscle insertion locations taken from a musculoskeletal model template and a scaling procedure to those obtained from a subject-specific model using an MRI of the subject. The problem was solved using OpenSim. Results showed that anatomical variability should be analysed from two perspectives. One the one hand, throughout the gait cycle, in a global way. On the other hand, at a characteristic instant of the gait cycle. Variations of ±1 cm in the position of the attachment points of certain muscles caused variations of up to 14.21% in averaged deviation of the muscle forces and 58.96% in the peak force in the modified muscle and variations up to 57.23% in the averaged deviation of the muscle force and up to 117.23% in the peak force in the rest of muscles. Then, the influence of that variability on muscle activity patterns and hip bone-on-bone forces could be described more precisely. A biomechanical analysis of a subject-specific musculoskeletal model was carried out. Using MRI data, variations up to 5 cm in the location of the insertion points were introduced. These modifications showed significant differences between the baseline model and the customized model: within the range [-12%, 10%] for muscle forces and around 35% of body weight for hip bone-on-bone forces.