Context: Objectively evaluating knee stability in multiple planes in individuals with anterior cruciate ligament injury may provide more comprehensive information than evaluating subjectively or in only a single plane. This could support both research and clinical decision making. However, for the clinical value of such an instrument to be evaluated, reliability and validity of the instrument must first be established. Despite multiple available instruments that measure rotational knee stability, it is not clear which of these instruments has adequate reliability and validity. Objective: We performed a systematic review to identify instruments for measuring rotational knee stability and to synthesize the available literature in which validity and reliability were evaluated. Evidence Acquisition: We searched 4 databases for publications reporting reliability or validity of an instrument designed to assess rotational knee stability. A narrative synthesis was used to present the results. Evidence Synthesis: We identified 42 studies evaluating 25 different instruments designed to measure movement while applying a standardized torque or while a tester performed a manual test (eg, pivot shift). There was high heterogeneity in parameters reported and criterion methods used. Intrarater and interrater reliability intraclass correlation coefficients were consistently adequate (>.75) except for when lower torques (ie, 6 N·m or less) were applied or acceleration or jerk was measured instead of laxity. Four out of 19 (21.1%) studies evaluating validity reported very good correlations (r > .8) with a criterion measure. Conclusions: We found no highquality evidence that provided sufficient evidence of both reliability and validity in any device. To evaluate the clinical benefit of objectively evaluating stability in multiple planes, further work is needed to develop, refine, and evaluate this class of devices.

Duchenne Muscular Dystrophy (DMD) progressively leads to loss of limb function due to muscle weakness. The incurable nature of the disease shifts the focus to improving quality of life, including assistive supports to improve arm function. Over time, the passive joint impedance (Jimp) of people with DMD increases. Force-based controlled motorised arm supports require a clear distinction between the user's movement intention and passive forces, such as passive Jimp. Therefore, Jimp identification is essential. This review aims to define Jimp, identify factors influencing it, and outline experimental methods used for quantification, with a focus on the upper extremities in DMD. A literature review was performed in May 2021 and updated in March 2024 using SCOPUS, PubMed, IEEEXplore, and WebOfScience. The results reveal confusion in definitions and show various Jimp measuring practices for both DMD and individuals without muscle weakness. This study presents an overview and lists important parameters affecting passive Jimp, such as the joint's position, velocity and the multi-articular nature of the upper arm muscles. For personalised passive Jimp compensation in arm supports, ramp-type perturbations with constant velocity across the full joint range appear most optimal for identifying the elevated and non-linear nature of the passive Jimp in DMD.

Introduction: By means of gait analysis and EMG measurements, we evaluated the difference between short and conventional hip stems in patients who underwent hip replacement for osteoarthritis. The remaining gait differences after hip replacements are well documented and caused by many factors, among which offset restoration is an important factor. Based on the theory that short stems are more capable of restoring offset, we compared gait between short stems and conventional stems. Methods: Two groups of ten patients were selected from ongoing trials and were case matched. For all patients, a detailed gait and EMG measurement was performed using the GRAIL (Gait Real-time Analysis Interactive lab, Motek ForceLink BV, Netherlands) system. Our primary outcome measurement was the peak hip abduction moment (HAM). Other gait parameters, muscular activity and the hip disability and osteoarthritis outcome score (HOOS) were secondary outcome measurements. Results: The peak HAM for the short stems was 1.29 Nm/kg (0.27) compared to 1.14 Nm/kg (0.32) for the conventional stem group (not significant). The short-stem group further showed longer step and stride length. Gluteus medius activation needed to stabilize the trunk and walking speed did not reach significance. The HOOS was better in the short-stem group. Conclusion: The results of our pilot study support the belief that a short stem can result in better outcomes, as measured by gait and EMG analysis.

Background: Most cases of toe walking in children are idiopathic. We used pathology-specific neuromusculoskeletal predictive simulations to identify potential underlying neural and muscular mechanisms contributing to idiopathic toe walking. Methods: A musculotendon contracture was added to the ankle plantarflexors of a generic musculoskeletal model to represent a pathology-specific contracture model, matching the reduced ankle dorsiflexion range-of-motion in a cohort of children with idiopathic toe walking. This model was employed in a forward dynamic simulation controlled by reflexes and supraspinal drive, governed by a multi-objective cost function to predict gait patterns with the contracture model. We validated the predicted gait using experimental gait data from children with idiopathic toe walking with ankle contracture, by calculating the root mean square errors averaged over all biomechanical variables. Findings: A predictive simulation with the pathology-specific model with contracture approached experimental ITW data (root mean square error = 1.37SD). Gastrocnemius activation was doubled from typical gait simulations, but lacked a peak in early stance as present in electromyography. This synthesised idiopathic toe walking was more costly for all cost function criteria than typical gait simulation. Also, it employed a different neural control strategy, with increased length- and velocity-based reflex gains to the plantarflexors in early stance and swing than typical gait simulations. Interpretation: The simulations provide insights into how a musculotendon contracture combined with altered neural control could contribute to idiopathic toe walking. Insights into these neuromuscular mechanisms could guide future computational and experimental studies to gain improved insight into the cause of idiopathic toe walking.

Segment coordinate systems (CSs) of marker-based multi-segment foot models are used to measure foot kinematics, however their relationship to the underlying bony anatomy is barely studied. The aim of this study was to compare marker-based CSs (MCSs) with bone morphology-based CSs (BCSs) for the hindfoot and forefoot. Markers were placed on the right foot of fifteen healthy adults according to the Oxford, Rizzoli and Amsterdam Foot Model (OFM, RFM and AFM, respectively). A CT scan was made while the foot was loaded in a simulated weight-bearing device. BCSs were based on axes of inertia. The orientation difference between BCSs and MCSs was quantified in helical and 3D Euler angles. To determine whether the marker models were able to capture inter-subject variability in bone poses, linear regressions were performed. Compared to the hindfoot BCS, all MCSs were more toward plantar flexion and internal rotation, and RFM was also oriented toward more inversion. Compared to the forefoot BCS, OFM and RFM were oriented more toward dorsal and plantar flexion, respectively, and internal rotation, while AFM was not statistically different in the sagittal and transverse plane. In the frontal plane, OFM was more toward eversion and RFM and AFM more toward inversion compared to BCS. Inter-subject bone pose variability was captured with RFM and AFM in most planes of the hindfoot and forefoot, while this variability was not captured by OFM. When interpreting multi-segment foot model data it is important to realize that MCSs and BCSs do not always align.

Background: Children with cerebral palsy often show deviating calf muscle activation patterns during gait, with excess activation during early stance and insufficient activation during push-off. Research question: Can children with cerebral palsy improve their calf muscle activation patterns during gait using one session of biofeedback-driven gaming? Methods: Eighteen children (6–17 y) with spastic cerebral palsy received implicit game-based biofeedback on electromyographic activity of the calf muscle (soleus or gastrocnemius medialis) while walking on a treadmill during one session. Biofeedback alternately aimed to reduce early stance activity, increase push-off activity, and both combined. Early stance and push-off activity and the double-bump-index (early stance divided by push-off activity) were determined during baseline and walking with feedback. Changes were assessed at group level using repeated measures ANOVA with simple contrast or Friedman test with post-hoc Wilcoxon signed rank test, as well as individually using independent t-tests or Wilcoxon rank sum tests. Perceived competence and interest-enjoyment were assessed through a questionnaire. Results: Children successfully decreased their electromyographic activity during early stance feedback trials (relative decrease of 6.8 ± 12.2 %, P = 0.025), with a trend during the combined feedback trials (6.5 ± 13.9 %, P = 0.055), and increased their electromyographic activity during push-off feedback trials (8.1 ± 15.8 %, P = 0.038). Individual improvements were seen in twelve of eighteen participants. All children experienced high levels of interest-enjoyment (8.4/10) and perceived competence (8.1/10). Significance: This exploratory study suggests that children with cerebral palsy can achieve small within-session improvements of their calf muscle activation pattern when provided with implicit biofeedback-driven gaming in an enjoyable manner. Follow-up gait training studies can incorporate this method to assess retention and long-term functional benefits of electromyographic biofeedback-driven gaming.

Background: The stiffness of a dorsal leaf AFO that minimizes walking energy cost in people with plantarflexor weakness varies between individuals. Using predictive simulations, we studied the effects of plantarflexor weakness, passive plantarflexor stiffness, body mass, and walking speed on the optimal AFO stiffness for energy cost reduction. Methods: We employed a planar, nine degrees-of-freedom musculoskeletal model, in which for validation maximal strength of the plantar flexors was reduced by 80%. Walking simulations, driven by minimizing a comprehensive cost function of which energy cost was the main contributor, were generated using a reflex-based controller. Simulations of walking without and with an AFO with stiffnesses between 0.9 and 8.7 Nm/degree were generated. After validation against experimental data of 11 people with plantarflexor weakness using the Root-mean-square error (RMSE), we systematically changed plantarflexor weakness (range 40–90% weakness), passive plantarflexor stiffness (range: 20–200% of normal), body mass (+ 30%) and walking speed (range: 0.8–1.2 m/s) in our baseline model to evaluate their effect on the optimal AFO stiffness for energy cost minimization. Results: Our simulations had a RMSE < 2 for all lower limb joint kinetics and kinematics except the knee and hip power for walking without AFO. When systematically varying model parameters, more severe plantarflexor weakness, lower passive plantarflexor stiffness, higher body mass and walking speed increased the optimal AFO stiffness for energy cost minimization, with the largest effects for severity of plantarflexor weakness. Conclusions: Our forward simulations demonstrate that in individuals with bilateral plantarflexor the necessary AFO stiffness for walking energy cost minimization is largely affected by severity of plantarflexor weakness, while variation in walking speed, passive muscle stiffness and body mass influence the optimal stiffness to a lesser extent. That gait deviations without AFO are overestimated may have exaggerated the required support of the AFO to minimize walking energy cost. Future research should focus on improving predictive simulations in order to implement personalized predictions in usual care. Trial Registration Nederlands Trial Register 5170. Registration date: May 7th 2015. http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5170.

Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8–23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0–1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut’s prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15–20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15–20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.

Balance recovery after tripping often requires an active adaptation of foot placement. Thus far, few attempts have been made to actively assist forward foot placement for balance recovery employing wearable devices. This study aims to explore the possibilities of active forward foot placement through two paradigms of actuation: assistive moments exerted with the reaction moments either internal or external to the human body, namely 'joint' moments and 'free' moments, respectively. Both paradigms can be applied to manipulate the motion of segments of the body (e.g., the shank or thigh), but joint actuators also exert opposing reaction moments on neighbouring body segments, altering posture and potentially inhibiting tripping recovery. We therefore hypothesised that a free moment paradigm is more effective in assisting balance recovery following tripping. The simulation software SCONE was used to simulate gait and tripping over various ground-fixed obstacles during the early swing phase. To aid forward foot placement, joint moments and free moments were applied either on the thigh to augment hip flexion or on the shank to augment knee extension. Two realizations of joint moments on the hip were simulated, with the reaction moment applied to either the pelvis or the contralateral thigh. The simulation results show that assisting hip flexion with either actuation paradigm on the thigh can result in full recovery of gait with a margin of stability and leg kinematics closely matching the unperturbed case. However, when assisting knee extension with moments on the shank, free moment effectively assist balance but joint moments with the reaction moment on the thigh do not. For joint moments assisting hip flexion, placement of the reaction moment on the contralateral thigh was more effective in achieving the desired limb dynamics than placing the reaction on the pelvis. Poor choice of placement of reaction moments may therefore have detrimental consequences for balance recovery, and removing them entirely (i.e., free moment) could be a more effective and reliable alternative. These results challenge conventional assumptions and may inform the design and development of a new generation of minimalistic wearable devices to promote balance during gait.

To maximize effects of dorsal leaf ankle foot orthoses (AFOs) on gait in people with bilateral plantarflexor weakness, the AFO properties should be matched to the individual. However, how AFO properties interact regarding their effect on gait function is unknown. We studied the interaction of AFO bending stiffness with neutral angle and footplate stiffness on the effect of bending stiffness on walking energy cost, gait kinematics and kinetics in people with plantarflexor weakness by employing predictive simulations. Our simulation framework consisted of a planar 11 degrees of freedom model, containing 11 muscles activated by a reflex-based neuromuscular controller. The controller was optimized by a comprehensive cost function, predominantly minimizing walking energy cost. The AFO bending and footplate stiffness were modelled as torsional springs around the ankle and metatarsal joint. The neutral angle of the AFO was defined as the angle in the sagittal plane at which the moment of the ankle torsional spring was zero. Simulations without AFO and with AFO for 9 bending stiffnesses (0–14 Nm/degree), 3 neutral angles (0–3-6 degrees dorsiflexion) and 3 footplate stiffnesses (0–0.5–2.0 Nm/degree) were performed. When changing neutral angle towards dorsiflexion, a higher AFO bending stiffness minimized energy cost of walking and normalized joint kinematics and kinetics. Footplate stiffness mainly affected MTP joint kinematics and kinetics, while no systematic and only marginal effects on energy cost were found. In conclusion, the interaction of the AFO bending stiffness and neutral angle in bilateral plantarflexor weakness, suggests that these should both be considered together when matching AFO properties to the individual patient.

Spasticity is a common impairment within pediatric neuromusculoskeletal disorders. How spasticity contributes to gait deviations is important for treatment selection. Our aim was to evaluate the pathophysiological mechanisms underlying gait deviations seen in children with spasticity, using predictive simulations. A cluster analysis was performed to extract distinct gait patterns from experimental gait data of 17 children with spasticity to be used as comparative validation data. A forward dynamic simulation framework was employed to predict gait with either velocity- or force-based hyperreflexia. This framework entailed a generic musculoskeletal model controlled by reflexes and supraspinal drive, governed by a multiobjective cost function. Hyperreflexia values were optimized to enable the simulated gait to best match experimental gait patterns. Three experimental gait patterns were extracted: (1) increased knee flexion, (2) increased ankle plantar flexion, and (3) increased knee flexion and ankle plantar flexion when compared with typical gait. Overall, velocity-based hyperreflexia outperformed force-based hyperreflexia. The first gait pattern could mostly be explained by rectus femoris and hamstrings velocity-based hyperreflexia, the second by gastrocnemius velocity-based hyperreflexia, and the third by gastrocnemius, soleus, and hamstrings velocity-based hyperreflexia. This study shows how velocity-based hyperreflexia from specific muscles contributes to different spastic gait patterns, which may help in providing targeted treatment.

Background: The Foot Profile Score (FPS) is a single score that summarises foot posture and dynamic foot motion during the gait cycle based on the kinematic data of the Oxford Foot Model. The FPS enables clinicians and researchers to quantify foot abnormalities during gait, to monitor change in foot/ankle motion over time, and to measure the outcome of intervention. With the creation of a new outcome measure, it is important to test its responsiveness in a clinical population for whom it may be sensitive to change. Aim: To evaluate the responsiveness of the FPS in a clinical population following isolated foot and ankle surgery. Methods: Using previous work completed to validate the FPS, we defined the minimal clinically important difference (MCID) for the FPS. Using this MCID, we applied it to a clinical population of 37 children with cerebral palsy, spastic hemiplegia, comparing their FPS before and after foot and ankle surgery. A regression analysis looked at potential relationships between the change in FPS and their pre-operative FPS, age at surgery, and time since surgery. Results: An MCID of 2.4 degrees was calculated through regression analysis. The mean change from the pre-operative FPS to the post-operative FPS was 4.6 (SD 3.7 with a range from −0.1 to 13.4). Twenty-eight children (76%) had a change in their FPS greater than the MCID. A regression analyses only showed a clear regression between pre-operative FPS and change in FPS (R2 = 0.58 p < 0.01).

Background: Gait adaptability is of utmost importance for keeping balance during gait in patients with knee osteoarthritis, also after total knee arthroplasty (TKA). The aims of this explorative study are: (1) assess the effect of age, knee osteoarthritis and TKA on gait adaptability; (2) assess changes in gait adaptability pre-to post-TKA and (3) their relation to functional outcomes. Methods: Gait adaptability was measured using a Target Stepping Test (TST) in knee osteoarthritis patients before (preTKA) and 12 months after TKA (postTKA) and compared to asymptomatic old (AsOld) and young adults (AsYng). TST imposed an asymmetrical gait pattern with projected stepping targets at high walking speed. Gait adaptability was determined through stepping accuracy on the targets. The Oxford Knee Score (OKS) and Timed-Up-and-Go test (TUG) measured patients’ physical function. Results: 12 preTKA, 8 postTKA, 18 AsYng, 21 AsOld were tested. Age showed no effect on TST-stepping accuracy. PreTKA showed worse TST-stepping accuracy compared to AsYng and AsOld (7.7; 6.2 cm difference). PostTKA showed an improvement of 52% in TST-stepping accuracy compared to preTKA (3.2 cm). Higher stepping accuracy preTKA predicted higher stepping accuracy post-TKA. In addition, low preTKA stepping accuracy predicted more improvement postTKA. Pre-to post-TKA improvement of stepping accuracy was related to improvement on the TUG (Beta = 0.17, p = 0.024), but not to OKS. Conclusions: Gait adaptability is improved following TKA in knee osteoarthritis patients and no longer significantly worse than asymptomatic adults. The relation of gait adaptability to function is shown by its relation to the TUG and shows to have predictive value pre-to post-TKA.

Background: Foot and ankle joint kinematics are measured during clinical gait analyses with marker-based multi-segment foot models. To improve on existing models, measurement errors due to soft tissue artifacts (STAs) and marker misplacements should be reduced. Therefore, the aim of this study is to define a clinically informed, universally applicable multi-segment foot model, which is developed to minimize these measurement errors. Methods: The Amsterdam foot model (AFM) is a follow-up of existing multi-segment foot models. It was developed by consulting a clinical expert panel and optimizing marker locations and segment definitions to minimize measurement errors. Evaluation of the model was performed in three steps. First, kinematic errors due to STAs were evaluated and compared to two frequently used foot models, i.e. the Oxford and Rizzoli foot models (OFM, RFM). Previously collected computed tomography data was used of 15 asymptomatic feet with markers attached, to determine the joint angles with and without STAs taken into account. Second, the sensitivity to marker misplacements was determined for AFM and compared to OFM and RFM using static standing trials of 19 asymptomatic subjects in which each marker was virtually replaced in multiple directions. Third, a preliminary inter- and intra-tester repeatability analysis was performed by acquiring 3D gait analysis data of 15 healthy subjects, who were equipped by two testers for two sessions. Repeatability of all kinematic parameters was assessed through analysis of the standard deviation (σ) and standard error of measurement (SEM). Results: The AFM was defined and all calculation methods were provided. Errors in joint angles due to STAs were in general similar or smaller in AFM (≤2.9°) compared to OFM (≤4.0°) and RFM (≤6.7°). AFM was also more robust to marker misplacement than OFM and RFM, as a large sensitivity of kinematic parameters to marker misplacement (i.e. > 1.0°/mm) was found only two times for AFM as opposed to six times for OFM and five times for RFM. The average intra-tester repeatability of AFM angles was σ:2.2[0.9°], SEM:3.3 ± 0.9° and the inter-tester repeatability was σ:3.1[2.1°], SEM:5.2 ± 2.3°. Conclusions: Measurement errors of AFM are smaller compared to two widely-used multi-segment foot models. This qualifies AFM as a follow-up to existing foot models, which should be evaluated further in a range of clinical application areas.

Osteoarthritis (OA) has a complex, heterogeneous and only partly understood etiology. There is a definite role of joint cartilage pathomechanics in originating and progressing of the disease. Although it is still not identified precisely enough to design or select targeted treatments, the progress of this year's research demonstrates that this goal became much closer. On multiple scales - tissue, joint and whole body - an increasing number of studies were done, with impressive results. (1) Technology based instrument innovations, especially when combined with machine learning models, have broadened the applicability of biomechanics. (2) Combinations with imaging make biomechanics much more precise & personalized. (3) The combination of Musculoskeletal & Finite Element Models yield valid personalized cartilage loads. (4) Mechanical outcomes are becoming increasingly meaningful to inform and evaluate treatments, including predictive power from biomechanical models. Since most recent advancements in the field of biomechanics in OA are at the level of a proof op principle, future research should not only continue on this successful path of innovation, but also aim to develop clinical workflows that would facilitate including precision biomechanics in large scale studies. Eventually this will yield clinical tools for decision making and a rationale for new therapies in OA.