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.

@en

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.

@en

Background: Walking speed is a confounding factor in biomechanical analyses of gait, but still many studies compare gait biomechanics at comfortable walking speed (CWS) that is likely to differ between groups or conditions. To identify gait deviation unrelated to walking speed, methods are needed to correct biomechanical data over the gait cycle for walking speed. Research question: How to compare knee kinetics over the gait cycle at different walking speeds? Methods: 22 asymptomatic subjects walked on a dual-belt treadmill at CWS and 4 fixed speeds. Knee moments in sagittal (KFM) and frontal plane (KAM) were calculated via inverse dynamics. The net moment differences between CWS and fixed speed were expressed as a root-mean-square error (RMSE) normalized to the range of the variable. Two methods to correct for walking speed were compared. In method 1, KFM and KAM values were estimated based on interpolation between speeds at each percentage of the gait cycle. In method 2, principal component analysis was used to extract speed related features to reconstruct KFM and KAM at the speed of interest. The accuracy of both methods was tested using a leave-one-out cross validation. Results: Walking speed influenced the magnitude and shape of KFM and KAM. To account for these speed influences using both methods, leave-one-out cross validation showed low normalized RMSE (< 5 %), with little difference between the two methods. RMSE for both reconstruction methods were up to 60 % lower than the RMSE between CWS and fixed speed. Significance: Both methods could accurately correct knee kinetics over the gait cycle for the effects of walking speed. Walking speed dependency should be incorporated in each gait laboratory's reference dataset to be able to identify gait deviations unrelated to gait speed.

@en

Understanding the effect of individual marker misplacements is important to improve the repeatability and aid to the interpretation of multi-segment foot models like the Oxford and Rizzoli Foot Models (OFM, RFM). Therefore, this study aimed to quantify the effect of controlled anatomical marker misplacement on multi-segment foot kinematics (i.e. marker placement sensitivity) as calculated by OFM and RFM in a range of foot sizes. Ten healthy adults and nine children were included. A combined OFM and RFM marker set was placed on their right foot and a static standing trial was collected. Each marker was replaced ± 10 mm in steps of 1 mm over the three axes of a foot coordinate system. For each replacement the change in segment orientation (tibia, hindfoot, midfoot, forefoot) was calculated with respect to the reference pose in which no markers were replaced. A linear fit was made to calculate the sensitivity of segment orientation to marker misplacement in °/mm. Additionally, the effect of foot size on the sensitivity was determined using linear regressions. For every foot segment of both models, at least one marker had a sensitivity ≥ 1.0°/mm. Highest values were found for the markers at the posterior aspect of the calcaneus in OFM (1.5°/mm) and the basis of the second metatarsal in RFM (1.4°/mm). Foot size had a small effect on 40% of the sensitivity values. This study identified markers of which consistent placement is critical to prevent clinically relevant errors (>5°). For more repeatable multi-segment models, the role of these markers within the models’ definitions needs to be reconsidered.

@en

Background: Patients with knee osteoarthritis can adapt their gait to unload the most painful knee joint in order to try to reduce pain and improve physical function. However, these gait adaptations can cause higher loads on the contralateral joints. The aim of the study was to investigate the interlimb differences in knee and hip frontal plane moments during gait in patients with knee osteoarthritis and in healthy controls. Methods: Forty patients with knee osteoarthritis and 19 healthy matched controls were measured during comfortable treadmill walking. Frontal plane joint moments were obtained of both hip and knee joints. Differences in interlimb moments within each group were assessed using statistical parametric mapping and discrete gait parameters. Findings: No interlimb differences were observed in patients with knee osteoarthritis and control subjects at group level. Furthermore, the patients presented similar interlimb variability as the controls. In a small subgroup (n = 12) of patients, the moments in the most painful knee were lower than in the contralateral knee, while the other patients (n = 28) showed higher moments in the most painful knee compared to the contralateral knee. However, no interlimb differences in the hip moments were observed within the subgroups. Interpretation: Patients with knee osteoarthritis do not have interlimb differences in knee and hip joint moments. Patients and healthy subjects demonstrate a similar interlimb variability in the moments of the lower extremities. In this context, differences in knee pain in patients with knee osteoarthritis did not induce any interlimb differences in the frontal plane knee and hip moments.

@en

Movement of skin markers with respect to their underlying bone (i.e. soft tissue artifacts (STAs)) might corrupt the accuracy of marker-based movement analyses. This study aims to quantify STAs in 3D for foot markers and their effect on multi-segment foot kinematics as calculated by the Oxford and Rizzoli Foot Models (OFM, RFM). Fifteen subjects with asymptomatic feet were seated on a custom-made loading device on a computed tomography (CT) table, with a combined OFM and RFM marker set on their right foot. One unloaded reference CT-scan with neutral foot position was performed, followed by 9 loaded CT-scans at different foot positions. The 3D-displacement (i.e. STA) of each marker in the underlying bone coordinate system between the reference scan and other scans was calculated. Subsequently, segment orientations and joint angles were calculated from the marker positions according to OFM and RFM definitions with and without STAs. The differences in degrees were defined as the errors caused by the marker displacements. Markers on the lateral malleolus and proximally on the posterior aspect of the calcaneus showed the largest STAs. The hindfoot-shank joint angle was most affected by STAs in the most extreme foot position (40° plantar flexion) in the sagittal plane for RFM (mean: 6.7°, max: 11.8°) and the transverse plane for OFM (mean: 3.9°, max: 6.8°). This study showed that STAs introduce clinically relevant errors in multi-segment foot kinematics. Moreover, it identified marker locations that are most affected by STAs, suggesting that their use within multi-segment foot models should be reconsidered.

@en

Knee joint instability is frequently reported by patients with knee osteoarthritis (KOA). Objective metrics to assess knee joint instability are lacking, making it difficult to target therapies aiming to improve stability. Therefore, the aim of this study was to compare responses in neuromechanics to perturbations during gait in patients with self-reported knee joint instability (KOA-I) versus patients reporting stable knees (KOA-S) and healthy control subjects. Forty patients (20 KOA-I and 20 KOA-S) and 20 healthy controls were measured during perturbed treadmill walking. Knee joint angles and muscle activation patterns were compared using statistical parametric mapping and discrete gait parameters. Furthermore, subgroups (moderate versus severe KOA) based on Kellgren and Lawrence classification were evaluated. Patients with KOA-I generally had greater knee flexion angles compared to controls during terminal stance and during swing of perturbed gait. In response to deceleration perturbations the patients with moderate KOA-I increased their knee flexion angles during terminal stance and pre-swing. Knee muscle activation patterns were overall similar between the groups. In response to sway medial perturbations the patients with severe KOA-I increased the co-contraction of the quadriceps versus hamstrings muscles during terminal stance. Patients with KOA-I respond to different gait perturbations by increasing knee flexion angles, co-contraction of muscles or both during terminal stance. These alterations in neuromechanics could assist in the assessment of knee joint instability in patients, to provide treatment options accordingly. Furthermore, longitudinal studies are needed to investigate the consequences of altered neuromechanics due to knee joint instability on the development of KOA.

@en

Purpose: To compare the responses in knee joint muscle activation patterns to different perturbations during gait in healthy subjects. Scope: Nine healthy participants were subjected to perturbed walking on a split-belt treadmill. Four perturbation types were applied, each at five intensities. The activations of seven muscles surrounding the knee were measured using surface EMG. The responses in muscle activation were expressed by calculating mean, peak, co-contraction (CCI) and perturbation responses (PR) values. PR captures the responses relative to unperturbed gait. Statistical parametric mapping analysis was used to compare the muscle activation patterns between conditions. Results: Perturbations evoked only small responses in muscle activation, though higher perturbation intensities yielded a higher mean activation in five muscles, as well as higher PR. Different types of perturbation led to different responses in the rectus femoris, medial gastrocnemius and lateral gastrocnemius. The participants had lower CCI just before perturbation compared to the same phase of unperturbed gait. Conclusions: Healthy participants respond to different perturbations during gait with small adaptations in their knee joint muscle activation patterns. This study provides insights in how the muscles are activated to stabilize the knee when challenged. Furthermore it could guide future studies in determining aberrant muscle activation in patients with knee disorders.

@en

Background: Gait analysis has been used for decades to quantify knee function in patients with knee osteoarthritis; however, it is unknown whether and to what extent inter-laboratory differences affect the comparison of gait data between studies. Therefore, the aim of this study was to perform an inter-laboratory comparison of knee biomechanics and muscle activation patterns during gait of patients with knee osteoarthritis. Methods: Knee biomechanics and muscle activation patterns from patients with knee osteoarthritis were analyzed, previously collected at Dalhousie University (DAL: n = 55) and Amsterdam UMC, VU medical center (VUmc: n = 39), using their in-house protocols. Additionally, one healthy male was measured at both locations. Both direct comparisons and after harmonization of components of the protocols were made. Inter-laboratory comparisons were quantified using statistical parametric mapping analysis and discrete gait parameters. Results: The inter-laboratory comparison showed offsets in the sagittal plane angles, moments and frontal plane angles, and phase shifts in the muscle activation patterns. Filter characteristics, initial contact identification and thigh anatomical frame definitions were harmonized between the laboratories. After this first step in protocol harmonization, the offsets in knee angles and sagittal plane moments remained, but the inter-laboratory comparison of the muscle activation patterns improved. Conclusions: Inter-laboratory differences obstruct valid comparisons of gait datasets from patients with knee osteoarthritis between gait laboratories. A first step in harmonization of gait analysis protocols improved the inter-laboratory comparison. Further protocol harmonization is recommended to enable valid comparisons between labs, data-sharing and multicenter trials to investigate knee function in patients with knee osteoarthritis.

@en

Background: After total knee replacement (TKR) some patients report low self-perceived function, which is clinically measured using patient reported outcome measures (PROMs). However, PROMs, e.g. the Oxford Knee Score (OKS), inherently lack objective parameters of knee function. Biomechanical gait analysis is an objective and reliable measurement to quantitatively assess joint function. Therefore, the aim of this study was to explore the relationship between biomechanical gait parameters and the OKS. Methods: Gait analyses were recorded in 37 patients at least one year after primary TKR and in 24 healthy controls. Parameters from this analysis were calculated for hip, knee and ankle joint angles and joint moments in the sagittal and frontal plane including initial contact, early, late stance and swing. For the patients these parameters were expressed as its difference to control values at matched walking speed. Linear regression analyses were performed between the parameters from gait analysis and the OKS, with speed as covariate. Results: The difference in knee extension angle at initial contact and late stance between patients and controls was significantly related to the OKS. Per one degree knee extension difference increase, the OKS reduced with 1.0 to 1.6 points. Overall, patients extended their knee less than controls. Neither ankle and hip gait parameters, nor joint moments showed a relation with OKS. Conclusions: All patients with a submaximal score on the OKS showed limited knee extension during gait, even without a mechanical constraint in knee extension. This could be related to motor control limitations in this patient group.

@en

Background: The Oxford Foot Model (OFM) and Rizzoli Foot Model (RFM) are the two most frequently used multi-segment models to measure foot kinematics. However, a comprehensive comparison of the kinematic output of these models is lacking. Research question: What are the differences in kinematic output between OFM and RFM during normal gait and typical pathological gait patterns in healthy adults?. Methods: A combined OFM and RFM marker set was placed on the right foot of ten healthy subjects. A static standing trial and six level walking trials were collected for normal gait and for four voluntarily adopted gait types: equinus, crouch, toe-in and toe-out. Joint angles were calculated for every trial for the hindfoot relative to shank (HF-SH), forefoot relative to hindfoot (FF-HF) and hallux relative to forefoot (HX-FF). Average static joint angles of both models were compared between models. After subtracting these offsets, the remaining dynamic angles were compared using statistical parametric mapping repeated measures ANOVAs and t-tests. Furthermore, range of motion was compared between models for every angle. Results: For the static posture, RFM compared to OFM measured more plantar flexion (Δ = 6°) and internal rotation (Δ = 7°) for HF-SH, more plantar flexion (Δ = 34°) and inversion (Δ = 13°) for FF-HF and more dorsal flexion (Δ = 37°) and abduction (Δ = 12°) for HX-FF. During normal walking, kinematic differences were found in various parts of the gait cycle. Moreover, range of motion was larger in the HF-SH for OFM and in FF-HF and HX-FF for RFM. The differences between models were not the same for all gait types. Equinus and toe-out gait demonstrated most pronounced differences. Significance: Differences are present in kinematic output between OFM and RFM, which also depend on gait type. Therefore, kinematic output of foot and ankle studies should be interpreted with careful consideration of the multi-segment foot model used.

@en

Objective: To evaluate muscle activation patterns and co-contraction around the knee in response to walking with modified gait patterns in patients with medial compartment knee-osteoarthritis (KOA). Design: 40 medial KOA patients walked on an instrumented treadmill. Surface EMG activity from seven knee-spanning muscles (gastrocnemius, hamstrings, quadriceps), kinematics, and ground reaction forces were recorded. Patients received real-time visual feedback on target kinematics to modify their gait pattern towards three different gait modifications: Toe-in, Wider steps, Medial Thrust. The individualized feedback aimed to reduce their first peak knee adduction moment (KAM) by ≥ 10%. Changes in muscle activations and medial/lateral co-contraction index during the loading response phase (10–35% of the gait cycle) were evaluated, for the steps in which ≥ 10% KAM reduction was achieved. Results: Data from 30 patients were included in the analyses; i.e. all who could successfully reduce their KAM in a sufficient number of steps by ≥ 10%. When walking with ≥ 10% KAM reduction, Medial Thrust gait (KAM − 31%) showed increased flexor activation (24%), co-contraction (17%) and knee flexion moment (35%). Isolated wider-step gait also reduced the KAM (− 26%), but to a smaller extent, but without increasing muscle activation amplitudes and co-contraction. Toe-in gait showed the greatest reduction in the KAM (− 35%), but was accompanied by an increased flexor activation of 42% and hence an increased co-contraction index. Conclusion: Gait modifications that are most effective in reducing the KAM also yield an increase in co-contraction, thereby compromising at least part of the effects on net knee load.

@en

Computed tomography (CT) imaging can be used to determine bone pose, sometimes combined with skin-mounted markers. For this specific application, a lower radiation dose than the conventional clinical dose might suffice. This study aims to determine how lowering the radiation dose of a CT-scan of the ankle and foot affects the precision of detecting bone pose and marker position. Radiation dose is proportional to tube charge. Hence, an adult cadaveric leg was scanned 10 times at four different tube charges (150, 75, 50 and 20 mAs) with a Philips Brilliance 64 CT scanner. Precision of detecting bone and marker position at 50 mAs was not significantly different from 75 mAs and from the clinically used 150 mAs, but higher than 20 mAs. Furthermore, no differences of the precision in detecting bone orientation were found. These results indicate that the radiation dose can be reduced by a factor 3 compared to the clinically usual radiation dose, without affecting the precision of detecting bone pose and marker position in the foot and ankle.@en

Background: Instability of the knee joint during gait is frequently reported by patients with knee osteoarthritis or an anterior cruciate ligament rupture. The assessment of instability in clinical practice and clinical research studies mainly relies on self-reporting. Alternatively, parameters measured with gait analysis have been explored as suitable objective indicators of dynamic knee (in)stability. Research question: This literature review aimed to establish an inventory of objective parameters of knee stability during gait. Methods: Five electronic databases (Pubmed, Embase, Cochrane, Cinahl and SPORTDiscuss) were systematically searched, with keywords concerning knee, stability and gait. Eligible studies used an objective parameter(s) to assess knee (in)stability during gait, being stated in the introduction or methods section. Out of 10717 studies, 89 studies were considered eligible. Results: Fourteen different patient populations were investigated with kinematic, kinetic and/or electromyography measurements during (challenged) gait. Thirty-three possible objective parameters were identified for knee stability, of which the majority was based on kinematic (14 parameters) or electromyography (12 parameters) measurements. Thirty-nine studies used challenged gait (i.e. external perturbations, downhill walking) to provoke knee joint instability. Limited or conflicting results were reported on the validity of the 33 parameters. Significance: In conclusion, a large number of different candidates for an objective knee stability gait parameter were found in literature, all without compelling evidence. A clear conceptual definition for dynamic knee joint stability is lacking, for which we suggest : “The capacity to respond to a challenge during gait within the natural boundaries of the knee”. Furthermore biomechanical gait laboratory protocols should be harmonized, to enable future developments on clinically relevant measure(s) of knee stability during gait.

@en

Objective: To evaluate whether improvement of proprioception, pain, or dynamic knee instability mediates the effect of wearing a soft knee brace on activity limitations in patients with knee osteoarthritis (OA). Methods: We conducted an analysis of data for 44 patients with knee OA who were enrolled in a laboratory-based trial evaluating the effect of wearing a commercially available soft knee brace. Activity limitations were assessed with the 10-meter walk test and the Get Up and Go test. Knee joint proprioception was assessed by an active joint position sense test; pain was assessed on a numeric rating scale (NRS) (range 0–10); pressure pain threshold (PPT) was assessed with a hand-held pressure algometer; dynamic knee instability was expressed by the perturbation response, i.e., a measure reflecting a deviation in mean knee varus–valgus angle after a controlled mechanical perturbation on a treadmill, with respect to level walking. Mediation analysis was conducted using the product of coefficients approach. Confidence intervals were calculated with a bootstrap procedure. Results: A decrease in pain (scored on an NRS) and a decrease in dynamic knee instability mediated the effect of wearing a soft knee brace on the reduction of activity limitations (P < 0.05), while changes in proprioception and PPT did not mediate this effect (P > 0.05). Conclusion: This study shows that decreased pain and reduced dynamic knee instability are pathways by which wearing a soft knee brace decreases activity limitations in patients with knee OA.

@en

Objectives Wearing a soft knee brace has been shown to reduce self-reported knee instability in persons with knee OA. There is a need to assess whether a soft knee brace has a beneficial effect on objectively assessed dynamic knee instability as well. The aims of the study were to evaluate the effect of a soft knee brace on objectively assessed dynamic knee instability and to assess the difference in effect between a non-tight and a tight soft knee brace in persons with knee OA. Methods Thirty-eight persons with knee OA and self-reported knee instability participated in a laboratory study. A within-subject design was used comparing no brace vs brace and comparing a non-tight vs a tight brace. The primary outcome measure was dynamic knee instability, expressed by the perturbation response (PR). The PR reflects deviation in the mean knee varus-valgus angle during level walking after a controlled mechanical perturbation. Linear mixed-effect model analysis was used to evaluate the effect of a brace on dynamic knee instability. Results Wearing a brace significantly reduced the PR compared with not wearing a brace (B = -0.16, P = 0.01). There was no difference between a non-tight and a tight brace (B = -0.03, P = 0.60). Conclusion This study is the first to report that wearing a soft knee brace reduces objectively assessed dynamic knee instability in persons with knee OA. Wearing a soft brace results in an objective improvement of knee instability beyond subjectively reported improvement. Trial registration Nederlands Trial register (trialregister.nl) NTR6363@en

Background: Gait modifications can reduce the knee adduction moment, a representation of knee loading. Reduced loading may help to slow progression of medial knee osteoarthritis. We aimed to investigate the response of patients with medial knee osteoarthritis to direct feedback on the knee adduction moment as a method for modifying the gait pattern, before and after training with specific gait modifications. Methods: Forty patients with medial knee osteoarthritis underwent 3D gait analysis on an instrumented-treadmill, while receiving real-time feedback on the peak knee adduction moment. Patients were trained with three different modifications; toe-in, wider steps and medial thrust gait. The response to real-time feedback on the knee adduction moment was measured before and after training. To evaluate the short term retention effect, we measured the changes without feedback. We also evaluated the effects on the knee flexion moment and at the hip and ankle joints. Findings: With direct feedback on the knee adduction moment, patients were initially unable to reduce the knee adduction moment. After training with specific modifications, peak knee adduction moment was reduced by 14% in response to direct feedback. Without feedback a 9% reduction in peak knee adduction moment was maintained. Hip moments were not increased with modified gait, but small increases in ankle adduction moment and knee flexion moment were observed. Interpretation: Real-time biofeedback directly on the knee adduction moment is a promising option for encouraging gait modifications to reduce knee loading, however only when combined with specific instructions on how to modify the gait.

@en

Individuals with cerebral palsy often walk with atypical gait patterns like equinus or crouch gait. Several multi-segment foot models have been developed [1] to measure the abnormal foot kinematics of these gait patterns. The Oxford Foot Model [2] (OFM) and Rizzoli Foot Model [3,4] (RFM) are used most frequently in both clinical practice and research [1]. Their different model characteristics (e.g. marker placement and axis definitions) will likely result in different kinematic output. However, these differences have not yet been extensively compared, especially not in pathological gait patterns.@en

The objective of this study was to investigate the learning process of knee osteoarthritis (KOA) patients learning to change their foot progression angle (FPA) over a six-week toe-in gait training program. Sixteen patients with medial KOA completed a six-week toe-in gait training program with real-time biofeedback. Patients walked on an instrumented treadmill while receiving real-time feedback on their foot progression angle (FPA) with reference to a target angle. The FPA difference (difference between target and actual FPA) was analyzed during i) natural walking, ii) walking with feedback, iii) walking without feedback and iv) walking with a dual-task at the start and end of the training program. Self-reported difficulty and abnormality and time spent walking and training were also analyzed. The FPA difference during natural walking was significantly decreased from median 6.9 to median 3.6° i.e. by 3.3° in week six (p < 0.001); adding feedback reduced FPA difference to almost zero. However the dual-task condition increased the FPA difference at week one compared to the feedback condition (median difference: 1.8°, p = 0.022), but after training this effect was minimized (median difference: 0.6°, p = 0.167). Self-reported abnormality and difficulty decreased from median 5 to 3 and from median 6 to 3 on the NRS respectively (p < 0.05). Patients with medial KOA could reduce the FPA difference during natural walking after the gait retraining program, with some evidence of a reduction in the cognitive demand needed to achieve this. Automation of adaptions might need support from more permanent feedback using wearable technologies.

@en

Background: Gait retraining interventions using real-time biofeedback have been proposed to alter the loading across the knee joint in patients with knee osteoarthritis. Despite the demonstrated benefits of these conservative treatments, their clinical adoption is currently obstructed by the high complexity, spatial demands, and cost of optical motion capture systems. In this study we propose and evaluate a wearable visual feedback system for gait retraining of the foot progression angle (FPA). Methods: The primary components of the system are inertial measurement units, which track the human movement without spatial limitations, and an augmented reality headset used to project the visual feedback in the visual field. The adapted gait protocol contained five different target angles ranging from 15 degrees toe-out to 5 degrees toe-in. Eleven healthy participants walked on an instrumented treadmill, and the protocol was performed using both an established laboratory visual feedback driven by optical motion capture, and the proposed wearable system. Results and conclusions: The wearable system tracked FPA with an accuracy of 2.4 degrees RMS and ICC=0.94 across all target angles and subjects, when compared to an optical motion capture reference. In addition, the effectiveness of the biofeedback, reflected by the number of steps with FPA value ±2 degrees from the target, was found to be around 50% in both wearable and laboratory approaches. These findings demonstrate that retraining of the FPA using wearable inertial sensing and visual feedback is feasible with effectiveness matching closely an established laboratory method. The proposed wearable setup may reduce the complexity of gait retraining applications and facilitate their transfer to routine clinical practice.

@en