Free-living and laboratory gait characteristics in patients with multiple sclerosis.

BACKGROUND: Wearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated. METHODS: Fourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5-6.0 and EDSS 5.5-5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5-50 steps), intermediate (iWB, 51-100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA. RESULTS: W-CWT compared to S-REF showed good gait event accuracy (0.05-0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%-58.2%) and continuous walking (11.2%-76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased. CONCLUSIONS: This study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking

Muscle recruitment strategies can reduce joint loading during level walking

Joint inflammation, with consequent cartilage damage and pain, typically reduces functionality and affects activities of daily life in a variety of musculoskeletal diseases. Since mechanical loading is an important determinant of the disease process, a possible conservative treatment is the unloading of joints. In principle, a neuromuscular rehabilitation program aimed to promote alternative muscle recruitments could reduce the loads on the lower-limb joints during walking. The extent of joint load reduction one could expect from this approach remains unknown. Furthermore, assuming significant reductions of the load on the affected joint can be achieved, it is unclear whether, and to what extent, the other joints will be overloaded. Using subject-specific musculoskeletal models of four different participants, we computed the muscle recruitment strategies that minimised the hip, knee and ankle contact force, and predicted the contact forces such strategies induced at the other joints. Significant reductions of the peak force and impulse at the knee and hip were obtained, while only a minimal effect was found at the ankle joint. Adversely, the peak force and the impulse in non-targeted joints increased when aiming to minimize the load in an adjacent joint. These results confirm the potential of alternative muscle recruitment strategies to reduce the loading at the knee and the hip, but not at the ankle. Therefore, neuromuscular rehabilitation can be targeted to reduce the loading at affected joints but must be considered carefully in patients with multiple joints affected due to the potential adverse effects in non-targeted joints

Variations of lower-limb joint kinematics associated with the use of different ankle joint models

Skeletal computational models relying on global optimisation are widely used alongside gait analysis for the estimate of joint kinematics, but the degrees of freedom (DOFs) and axes definitions to model the ankle complex are still debated. The aim of this paper is to establish whether ankle modelling choices would also critically affect the estimate of the other joints' kinematics. Gait and MRI data from fifteen juvenile participants were used to implement three ankle joint models (M1, one-DOF sagittal motion; M2, two-DOFs sagittal and frontal motions; M3, three-DOFs) as part of a full lower-limb skeletal model. Differences in lower-limb joint and foot progression angles calculated using global optimisation were evaluated both at individual and group level. Furthermore, the influence of these differences on the correlations between joints and on the calculations of the root mean square deviation (RMSD) were investigated. Inter-model variations at individual level reached up to 4.2°, 9.1°, and 15.0° for hip flexion, adduction, and rotation, respectively, and up to 6.5° for knee flexion. Despite the tibiotalar axis being the same for all models, up to 19.3° (9.1° on average) larger dorsiflexion was found at push-off with M2. A stronger correlation between foot progression and ankle and knee sagittal movements was found for M1. Finally, RMSD led to inconsistent ranking of the participants when using different models. In conclusion, the choice of the ankle joint model affects the estimates of proximal lower-limb joint kinematics, which should discourage comparisons across datasets built with different models

Wearable sensors can reliably quantify gait alterations associated with disability in people with progressive multiple sclerosis in a clinical setting

Gait disability in people with progressive multiple sclerosis (MS) is difficult to quantify using existing clinical tools. This study aims to identify reliable and objective gait-based biomarkers to monitor progressive multiple sclerosis (MS) in clinical settings. During routine clinical visits, 57 people with secondary progressive MS and 24 healthy controls walked for 6 minutes wearing three inertial motion sensors. Fifteen gait measures were computed from the sensor data and tested for between-session reliability, for differences between controls and people with moderate and severe MS disability, and for correlation with Expanded Disability Status Scale (EDSS) scores. The majority of gait measures showed good to excellent between-session reliability when assessed in a subgroup of 23 healthy controls and 25 people with MS. These measures showed that people with MS walked with significantly longer step and stride durations, reduced step and stride regularity, and experienced difficulties in controlling and maintaining a stable walk when compared to controls. These abnormalities significantly increased in people with a higher level of disability and correlated with their EDSS scores. Reliable and objective gait-based biomarkers using wearable sensors have been identified. These biomarkers may allow clinicians to quantify clinically relevant alterations in gait in people with progressive MS within the context of regular clinical visits

A systematic review of the gait characteristics associated with Cerebellar Ataxia

BACKGROUND: Cerebellar Ataxias are a group of gait disorders resulting from dysfunction of the cerebellum, commonly characterised by slowly progressing incoordination that manifests as problems with balance and walking leading to considerable disability. There is increasing acceptance of gait analysis techniques to quantify subtle gait characteristics that are unmeasurable by current clinical methods This systematic review aims to identify the gait characteristics able to differentiate between Cerebellar Ataxia and healthy controls. METHODS: Following systematic search and critical appraisal of the literature, gait data relating to preferred paced walking in Cerebellar Ataxia was extracted from 21 studies. A random-effect model meta-analysis was performed for 14 spatiotemporal parameters. Quality assessment was completed to detect risk of bias. RESULTS: There is strong evidence that compared with healthy controls, Cerebellar Ataxia patients walk with a reduced walking speed and cadence, reduced step length, stride length, and swing phase, increased walking base width, stride time, step time, stance phase and double limb support phase with increased variability of step length, stride length, and stride time. CONCLUSION: The consensus description provided here, clarifies the gait pattern associated with ataxic gait disturbance in a large cohort of participants. High quality research and reporting is needed to explore specific genetic diagnoses and identify biomarkers for disease progression in order to develop well-evidenced clinical guidelines and interventions for Cerebellar Ataxia

Personalised 3D knee compliance from clinically viable knee laxity measurements: A proof of concept ex vivo experiment.

Personalised information of knee mechanics is increasingly used for guiding knee reconstruction surgery. We explored use of uniaxial knee laxity tests mimicking Lachman and Pivot-shift tests for quantifying 3D knee compliance in healthy and injured knees. Two healthy knee specimens (males, 60 and 88 years of age) were tested. Six-degree-of-freedom tibiofemoral displacements were applied to each specimen at 5 intermediate angles between 0° and 90° knee flexion. The force response was recorded. Six-degree-of-freedom and uniaxial tests were repeated after sequential resection of the anterior cruciate, posterior cruciate and lateral collateral ligament. 3D knee compliance (C6DOF) was calculated using the six-degrees-of-freedom measurements for both the healthy and ligament-deficient knees and validated using a leave-one-out cross-validation. 3D knee compliance (CCT) was also calculated using uniaxial measurements for Lachman and Pivot-shift tests both conjointly and separately. C6DOF and CCT matrices were compared component-by-component and using principal axes decomposition. Bland-Altman plots, median and 40-60th percentile range were used as measurements of bias and dispersion. The error on tibiofemoral displacements predicted using C6DOF was < 9.6% for every loading direction and after release of each ligament. Overall, there was good agreement between C6DOF and CCT components for both the component-by-component and principal component comparison. The dispersion of principal components (compliance coefficients, positions and pitches) based on both uniaxial tests was lower than that based on single uniaxial tests. Uniaxial tests may provide personalised information of 3D knee compliance

Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease

Background Changes in upper body (UB) motion during gait may be a marker of incipient pathology, intervention response and disease progression in Parkinson’s disease (PD), which if independent from the lower body motion, might provide an improved assessment of gait. Research question This study aimed to test this hypothesis and establish whether variables calculated from accelerations measured on the UB are unique from spatiotemporal characteristics and can contribute to an improved classification of PD gait. Methods Data was obtained from 70 people with PD (69.2 ± 9.9 y.o., UPDRS III: 36.9 ± 12.3) and 64 age-matched controls (71.6 ± 6.8 y.o.). Spatiotemporal characteristics were measured using a pressure sensitive mat (GAITRite). Head and pelvis accelerations were synchronously measured with wearable inertial sensors (Opal, APDM). Pearson’s product-moment correlations were calculated between 49 selected variables from UB accelerations (representing magnitude, smoothness, regularity, symmetry and attenuation) and 16 traditional spatiotemporal characteristics (representing pace, variability, rhythm, asymmetry and postural control). Univariate and multivariate regression analysis was used to test the variables ability to classify PD gait. Results The variables were mostly unique from each other (67% of variables recorded an r < 0.3). Univariate and multivariate analysis showed that UB variables were moderately better at classifying PD gait than the spatiotemporal characteristics (Univariate: 0.70 to 0.81, Multivariate: 0.88 to 0.91 AUC). Significance This study showed for the first time that, if aiming at objective and optimal sensitive biomarkers for PD, UB variables should be measured in conjunction with spatiotemporal characteristics to obtain a more holistic assessment of PD gait for use in a clinical or free-living environment

Reducing the complexity of musculoskeletal models using gaussian process emulators

Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies (e.g., osteoarthritis). Subject-specific MSKMs offer more accurate predictions than their scaled-generic counterparts. This accuracy is achieved through time-consuming personalisation of models and manual tuning procedures that suffer from potential repeatability errors, hence limiting the wider application of this modelling approach. In this work we have developed a methodology relying on Sobol’s sensitivity analysis (SSA) for ranking muscles based on their importance to the determination of the joint contact forces (JCFs) in a cohort of older women. The thousands of data points required for SSA are generated using Gaussian Process emulators, a Bayesian technique to infer the input–output relationship between nonlinear models from a limited number of observations. Results show that there is a pool of muscles whose personalisation has little effects on the predictions of JCFs, allowing for a reduced but still accurate representation of the musculoskeletal system within shorter timeframes. Furthermore, joint forces in subject-specific and generic models are influenced by different sets of muscles, suggesting the existence of a model-specific component to the sensitivity analysis