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Abstract Clinical gait analysis is an important biomechanics field that is often influenced by subjectivity in time-varying analysis leading to type I and II errors. Statistical Parametric Mapping can operate on all time-varying joint dynamics simultaneously, thereby overcoming subjectivity errors. We present MovementRx, the first gait analysis modelling application that correctly models the deviations of joints kinematics and kinetics both in 3 and 1 degrees of freedom; presented with easy-to-understand color maps for clinicians with limited statistical training. MovementRx is a python-based versatile GUI-enabled movement analysis decision support system, that provides a holistic view of all lower limb joints fundamental to the kinematic/kinetic chain related to functional gait. The user can cascade the view from single 3D multivariate result down to specific single joint individual 1D scalar movement component in a simple, coherent, objective, and visually intuitive manner. We highlight MovementRx benefit by presenting a case-study of a right knee osteoarthritis (OA) patient with otherwise undetected postintervention contralateral OA predisposition. MovementRx detected elevated frontal plane moments of the patient’s unaffected knee. The patient also revealed a surprising adverse compensation to the contralateral limb.

A high contact force between the medial femoral condyle and the tibial plateau is the primary cause of medial compartment knee osteoarthritis (OA). A high medial contact force (MCF) during gait has been shown to be correlated to both the knee adduction moment (KAM) and knee flexion/extension moment (KFM). In this study, we used OpenSim Moco to find gait kinematics that reduced the peaks of the KAM, without increasing the peaks of the KFM, which could potentially reduce the MCF and, hence, the progression of knee OA. We used gait data from four knee OA participants. Our simulations decreased both peaks of the KAM without increasing either peak of the KFM. We found that increasing the step width was the primary mechanism, followed by simulations of all participants to reduce the frontal plane lever arm of the ground reaction force vector about the knee, in turn reducing the KAM. Importantly, each participant simulation followed different patterns of kinematic changes to achieve this reduction, which highlighted the need for participant-specific gait modifications. Moreover, we were able to simulate emerging gait patterns within 15 min, enhancing the relevance and potential for the application of developed methods in clinical settings.

SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using “Hotelling 2” and “T test 2” statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients’ group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis.