The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

Gait measurements in the transverse plane using a wearable system: An experimental study of test-retest reliability

3D gait analysis comprises the study of kinematics in the sagittal, coronal, and transverse planes. The transverse plane measurements are usually less used and generally show the lowest reliability. Nevertheless, the knee and ankle joint center trajectories, in the transverse plane, provide new parameters that may be important in clinical gait analysis. The aim of this study is to analyze the test-retest variability of these parameters. Gait measurements were performed using H-Gait, a wearable system based on magnetic and inertial sensors. A normal weight and an overweight subject were recruited and were asked to walk at their preferred speed for 6 trials. For both of them, the angle between the right and left knee and ankle joint center trajectories were analyzed. Overall, results showed a standard deviation across trials always lower than 2°. This small standard deviation was found also in the overweight subject, for whom it is usually challenging to obtain reliable gait measurements. In addition, a greater knee angle between the right and left joint center trajectories was found in the overweight subject compared to the normal weight. The promising results of this study suggest that the new parameters introduced might be suitable to assess gait of subjects with different anthropometric characteristics

Which osteoarthritic gait features recover following total knee replacement surgery?

Background Gait analysis can be used to measure variations in joint function in patients with knee osteoarthritis (OA), and is useful when observing longitudinal biomechanical changes following Total Knee Replacement (TKR) surgery. The Cardiff Classifier is an objective classification tool applied previously to examine the extent of biomechanical recovery following TKR. In this study, it is further developed to reveal the salient features that contribute to recovery towards healthy function. Methods Gait analysis was performed on 30 patients before and after TKR surgery, and 30 healthy controls. Median TKR follow-up time was 13 months. The combined application of principal component analysis (PCA) and the Cardiff Classifier defined 18 biomechanical features that discriminated OA from healthy gait. Statistical analysis tested whether these features were affected by TKR surgery and, if so, whether they recovered to values found for the controls. Results The Cardiff Classifier successfully discriminated between OA and healthy gait in all 60 cases. Of the 18 discriminatory features, only six (33%) were significantly affected by surgery, including features in all three planes of the ground reaction force (p<0.001), ankle dorsiflexion moment (p<0.001), hip adduction moment (p = 0.003), and transverse hip angle (p = 0.007). All but two (89%) of these features remained significantly different to those of the control group after surgery. Conclusions This approach was able to discriminate gait biomechanics associated with knee OA. The ground reaction force provided the strongest discriminatory features. Despite increased gait velocity and improvements in self-reported pain and function, which would normally be clinical indicators of recovery, the majority of features were not affected by TKR surgery. This TKR cohort retained pre-operative gait patterns; reduced sagittal hip and knee moments, decreased knee flexion, increased hip flexion, and reduced hip adduction. The changes that were associated with surgery were predominantly found at the ankle and hip, rather than at the knee

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Despite good clinical functional outcome, deficits in gait biomechanics exist 2 years after total hip replacement surgery. The aims of this research were (1) to group patients showing similar gait adaptations to hip osteoarthritis and (2) to investigate the effect of the surgical treatment on gait kinematics and external joint moments. In a secondary analysis, gait data of 51 patients with unilateral hip osteoarthritis were analyzed. A k-means cluster analysis was performed on scores derived via a principal component analysis of the gait kinematics. Preoperative and postoperative datasets were statistically tested between clusters and 46 healthy controls. The first three principal components incorporated hip flexion/extension, pelvic tilt, foot progression angle and thorax tilt. Two clusters were discriminated best by the peak hip extension during terminal stance. Both clusters deviated from healthy controls in spatio-temporal, kinematic and kinetic parameters. The cluster with less hip extension deviated significantly more. The clusters improved postoperatively but differences to healthy controls were still present one year after surgery. A poor preoperative gait pattern in patients with unilateral hip osteoarthritis is associated with worse gait kinematics after total hip replacement. Further research should focus on the identification of patients who can benefit from an adapted or individualized rehabilitation program

Prediction and control in human neuromusculoskeletal models

Computational neuromusculoskeletal modelling enables the generation and testing of hypotheses about human movement on a large scale, in silico. Humanoid models, which increasingly aim to replicate the full complexity of the human nervous and musculoskeletal systems, are built on extensive prior knowledge, extracted from anatomical imaging, kinematic and kinetic measurement, and codified as model description. Where inverse dynamic analysis is applied, its basis is in Newton's laws of motion, and in solving for muscular redundancy it is necessary to invoke knowledge of central nervous motor strategy. This epistemological approach contrasts strongly with the models of machine learning, which are generally over-parameterised and largely data-driven. Even as spectacular performance has been delivered by the application of these models in a number of discrete domains of artificial intelligence, work towards general human-level intelligence has faltered, leading many to wonder if the data-driven approach is fundamentally limited, and spurring efforts to combine machine learning with knowledge-based modelling. Through a series of five studies, this thesis explores the combination of neuromusculoskeletal modelling with machine learning in order to enhance the core tasks of prediction and control. Several principles for the development of clinically useful artificially intelligent systems emerge: stability, computational efficiency and incorporation of prior knowledge. The first study concerns the use of neural network function approximators for the prediction of internal forces during human movement, an important task with many clinical applications, but one for which the standard tools of modelling are slow and cumbersome. By training on a large dataset of motions and their corresponding forces, state of the art performance is demonstrated, with many-fold increases in inference speed enabling the deployment of trained models for use in a real time biofeedback system. Neural networks trained in this way, to imitate some optimal controller, encode a mapping from high-level movement descriptors to actuator commands, and may thus be deployed in simulation as \textit{policies} to control the actions of humanoid models. Unfortunately, the high complexity of realistic simulation makes stable control a challenging task, beyond the capabilities of such naively trained models. The objective of the second study was to improve performance and stability of policy-based controllers for humanoid models in simulation. A novel technique was developed, borrowing from established unsupervised adversarial methods in computer vision. This technique enabled significant gains in performance relative to a neural network baseline, without the need for additional access to the optimal controller. For the third study, increases in the capabilities of these policy-based controllers were sought. Reinforcement learning is widely considered the most powerful means of optimising such policies, but it is computationally inefficient, and this inefficiency limits its clinical utility. To mitigate this problem, a novel framework, making use of domain-specific knowledge present in motion data, and in an inverse model of the biomechanical system, was developed. Training on simple desktop hardware, this framework enabled rapid initialisation of humanoid models that were able to move naturally through a 3-dimensional simulated environment, with 900-fold improvements in sample efficiency relative to a related technique based on pure reinforcement learning. After training with subject-specific anatomical parameters, and motion data, learned policies represent personalised models of motor control that may be further interrogated to test hypotheses about movement. For the fourth study, subject-specific controllers were taken and used as the substrate for transfer learning, by removing kinematic constraints and optimising with respect to the magnitude of the medial knee joint reaction force, an important biomechanical variable in osteoarthritis of the knee. Models learned new kinematic strategies for the reduction of this biomarker, which were subsequently validated by their use, in the real world, to construct subject-specific routines for real time gait retraining. Six out of eight subjects were able to reduce medial knee joint loading by pursuing the personalised kinematic targets found in simulation. Personalisation of assistive devices, such as limb prostheses, is another area of growing interest, and one for which computational frameworks promise cost-effective solutions. Reinforcement learning provides powerful techniques for this task but the expansion of the scope of optimisation, to include previously static elements of a prosthesis, is problematic for its complexity and resulting sample inefficiency. The fifth and final study demonstrates a new algorithm that leverages the methods described in the previous studies, and additional techniques for variance control, to surmount this problem, improving sample efficiency and simultaneously, through the use of prior knowledge encoded in motion data, providing a rational means of determining optimality in the prosthesis. Trained models were able to jointly optimise motor control and prosthesis design to enable improved performance in a walking task, and optimised designs were robust to both random seed and reward specification. This algorithm could be used to speed the design and production of real personalised prostheses, representing a potent realisation of the potential benefits of combined reinforcement learning and realistic neuromusculoskeletal modelling.Open Acces

Kinematic and kinetic analysis of transfemoral prosthesis

The feasibility of using transfemoral prosthesis Otto bock with 3R80 knee and articulated ankle1C30 “Trias” was analyzed from the perspective of dynamics and clinic. The kinematic and kinetic study of gait were performed on 5 amputated volunteers and 5 controls using videography techniques and force platform. Kinetic asymmetry gait is one of the main causes of hip joint degeneration. Combining kinematic and kinetic variables, we can draw important conclusions related to the dynamic imbalance of the main causes of hip degenerative diseases through the clinical trials of radiography film and density measurement, which has become an important tool to evaluate the feasibility of prosthetic design

Anticipatory Postural Adjustments During Lateral Step Motion in Patients With Hip Osteoarthritis

Patients with hip osteoarthritis (OA) have difficulty with mediolateral postural control. Since the symptom of hip OA includes joint pain, which mostly occurs upon initial movement, patients with hip OA might have disabling problems with movement initiation. This study aimed to identify the movement strategy during the anticipatory postural adjustments in the lateral step motion in patients with hip OA. We studied 18 female subjects with unilateral hip OA and 10 healthy subjects, and measured temporal, kinetic, and kinematic variables. Patients with hip OA required a longer duration of anticipation phase than the control subjects, the total duration of lateral stepping was not different between the groups. Displacement of the center of mass to the supporting (affected) side during the anticipation phase was not different between the two groups. These findings suggest that, in patients with hip OA, the center of mass slowly moved to the affected side. Furthermore, patients with hip OA showed greater shift of the trunk to the supporting side than did the control subjects. These movement characteristics might contribute to the achievement of both protection of the affected hip joint and quickness in the subsequent lateral step in patients with hip OA

Quantifying Joint Coordination Variability in Anterior Cruciate Ligament-Reconstructed Individuals During Walking

The knee is the second most common joint to sustain injury. An estimated 200,000 anterior cruciate ligament (ACL) ruptures occur each year in the United States alone, and about 100,000 ACL reconstruction (ACLR) surgeries are performed annually. There is a significant risk of developing osteoarthritis of the knee after incurring an ACL injury, and the incidence of ipsilateral or contralateral injury is six times greater in individuals who have a surgically repaired ACL. Past studies have analyzed kinetic and kinematic characteristics of individual lower extremity joints to reveal differences between subjects with and without ACLR. Despite reports of altered kinematic performance in individuals with ACLR compared to healthy controls, most of the analyses did not evaluate coordinative function, and thus neglected to consider how the lower limb acts as a linked chain. Therefore, the present study used a method based on dynamical systems theory to quantify coordination and account for the interaction between joints in the lower extremity. The purpose of the study was to quantify and compare joint coordination variability and joint coordination patterns between individuals with ACLR and matched controls. Institutional Review Board (IRB) approval was obtained prior to data collection, and all subjects signed an informed consent form. Twenty subjects (nine females, eleven males; body mass index (BMI) 25±3.5 kg/m2) who had undergone unilateral ACLR (thirteen right, seven left) and been cleared to return to full activity were compared to twenty control subjects matched by gender and BMI (nine females, eleven males; BMI 22.4±2.4 km/m2). Kinematic and kinetic data during walking were collected in the UTHSC Motion Analysis Laboratory. A vector coding technique was used to calculate coupling angles for six joint couplings involving the hip, knee, and ankle across four periods within the stance phase. Joint coordination variability was defined as the standard deviation of the coupling angle between trials within a subject, and joint coordination patterns were based on coupling angle magnitude. Individuals with ACLR exhibited increased joint coordination variability and altered joint coordination patterns compared to the matched controls during the stance phase of walking. These results suggested that coordinative function may not be fully restored in individuals with ACLR following rehabilitation. Increased coordination variability from a normal, or optimal amount as well as altered coordination patterns may result from a deficit in sensorimotor control, and represent risk of re-injury. Further investigation that is prospective, focuses primarily on hip-knee coupled motion in frontal and transverse planes, and includes assessment of EMG in addition to kinematics may contribute relevant information for improving ACL injury prevention and rehabilitation