Variability of the lower limb symmetry index associated with the gait parameters in the overweight adult population with flatfoot: a case-control study

Background: Adult acquired flatfoot is characterized by a medial arch collapse during monopodal support in the stance phase, developing eversion of the calcaneus and abduction of the forefoot linked to the hindfoot. The purpose of our research was to analyze the dynamic symmetry index in the lower limbs comparing patients with flatfoot and normal foot. Methods: A case-control study was carried out with a sample of 62 participants divided into two groups consisting of 31 participants were overweight with bilateral flatfoot and 31 participants with healthy feet. A portable plantar pressure platform with piezoresistive sensors was used to measure the load symmetry index in the lower limbs in the foot areas and gait phases. Results: Gait pattern analysis showed statistically significant differences in the symmetry index for lateral load (p = 0.004), the initial contact phase (p = 0.025) and the forefoot phase (p < 0.001). Conclusion: The adults were overweight with bilateral flatfoot evidenced alterations in the symmetry index in the lateral load and in the initial contact and flatfoot contact phases, showing greater instability in overweight adult flatfoot compared to the people with normal feet.

Evaluation of Gait Pattern and Lower Extremity Kinematics of Children with Morquio Syndrome (MPS IV)

Morquio syndrome (mucopolysaccharidosis IV/MPS IV) is a genetic disorder leading to skeletal abnormalities and gait deviations. Research on the gait patterns and lower extremity physical characteristics associated with skeletal dysplasia in children with MPS IV is currently limited. This research aimed to provide baseline gait patterns and lower limb skeletal alignment of children with MPS IV utilizing three-dimensional instrumented gait analysis. This Institutional Review Board-approved retrospective study evaluates the kinematics of the lower extremities of children with MPS IV during gait, comparing them with an age-matched group of typically developing children. Thirty-three children with MPS IV were included (8.6 ± 4.0 years old). Children with MPS IV walk with increased anterior pelvic tilt, knee valgus, knee flexion, external tibial torsion, and reduced walking speed and stride length (p < 0.001). Multiplanar abnormal alignment results in abnormal knee moments (p < 0.001). Limited correlations exist (r = 0.69–0.28) between dynamic three-dimensional measurements of knee varus/valgus and rotational alignment and traditional static two-dimensional measures (physical examination or radiographs) suggesting the possibility of knee instability during gait and the benefits of dynamic assessment.

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

Gait Quality Assessment in Survivors from Severe Traumatic Brain Injury: An Instrumented Approach Based on Inertial Sensors

Despite existing evidence that gait disorders are a common consequence of severe traumatic brain injury (sTBI), the literature describing gait instability in sTBI survivors is scant. Thus, the present study aims at quantifying gait patterns in sTBI through wearable inertial sensors and investigating the association of sensor-based gait quality indices with the scores of commonly administered clinical scales. Twenty healthy adults (control group, CG) and 20 people who suffered from a sTBI were recruited. The Berg balance scale, community balance and mobility scale, and dynamic gait index (DGI) were administered to sTBI participants, who were further divided into two subgroups, severe and very severe, according to their score in the DGI. Participants performed the 10 m walk, the Figure-of-8 walk, and the Fukuda stepping tests, while wearing five inertial sensors. Significant differences were found among the three groups, discriminating not only between CG and sTBI, but also for walking ability levels. Several indices displayed a significant correlation with clinical scales scores, especially in the 10 m walking and Figure-of-8 walk tests. Results show that the use of wearable sensors allows the obtainment of quantitative information about a patient’s gait disorders and discrimination between different levels of walking abilities, supporting the rehabilitative staff in designing tailored therapeutic interventions.

Gait Classification in Unilateral Cerebral Palsy

As unilateral cerebral palsy represents a complex disorder, gait classification is difficult. Knowledge of the most frequent gait patterns and functional impairment is crucial for proper decision-making. This study analyzes the prevalence of gait patterns as well as the relation of different gait patterns and the Gross Motor Function Classification System (GMFCS). Eighty-nine patients were classified retrospectively using the GMFCS, the classification of Winters, Gage, and Hicks (WGH), and Sutherland et al. The distribution of GMFCS levels among the different gait patterns was analyzed using Chi-squared test. The most common subtypes were GMFCS level I, WGH type I, and recurvatum knee. Seventeen percent (WGH) and 59% (Sutherland) of the patients did not match any criteria. Applying both classifications complementarily reduced the number of unclassified patients significantly. There was no significant difference concerning the distribution of GMFCS levels or age among the different gait patterns. A combined use of various classification systems is beneficial for proper decision-making. Unclassified patients seem to be a heterogeneous subgroup concerning functional impairment. There is a need of further characterization of the unclassifiable gait patterns and the caused functional impairment. Instrumented gait analysis remains the gold standard and should be broadly used for future studies and in clinical practice.

Gait Pattern Differences Among Children With Bilateral Cerebral Palsy

Background: The positive findings from our previous studies, which revealed the link between postural and gait patterns in children with unilateral cerebral palsy (CP) were very encouraging for recognition this relationship in children with bilateral cerebral palsy (CP). Therefore, the objective of this study was to evaluate whether different gait patterns corresponding to postural patterns in children with bilateral CP could be statistically significant according to a cluster analysis. Methods: Fifty-eight participants with bilateral CP and 45 matched children with typical growth and development. The participants walked barefoot along a treadmill at their own pace. Three-dimensional kinematic data were collected using the Measuring System for Motion Analysis. To characterize gait patterns, the Gillette Gait Index (GGI) and its 16 distinct gait parameters were used. The participants were divided into four subgroups according to their postural patterns. Results: A cluster analysis revealed 4 gait patterns corresponding to postural patterns: (1) normal gait pattern corresponded to neutral posture; (2) balanced gait pattern corresponded to balanced posture; (3) lordotic gait pattern corresponded to lordotic postural pattern; (4) swayback gait pattern corresponded to backward-leaning posture. There were significant differences in mean GGI and various clusters in the 8 GGI gait parameters: cadence, mean pelvic tilt; mean pelvic rotation, minimum hip flexion, peak hip abduction in swing; knee flexion at initial contact, and peak dorsiflexion in stance. Conclusion: Our results showed that gait discrepancies among children with bilateral CP were not simply a result of lower limb kinematic deviations in the sagittal plane. Information on different gait patterns could improve early therapy in children with bilateral CP before abnormal gait patterns are fully established.

3D characterisation of the dynamics of foot joints of adults during walking. Gait pattern identification

A detailed description of the kinematics and kinetics of the ankle, midtarsal and metatarsophalangeal joints of the feet of a healthy adult male population during barefoot walking is provided. Plots of the angles and moments in each plane during the stance phase are reported, along with the mean and standard deviation values of 87 different parameters that characterise the 3D dynamics of the foot joints. These parameters were used to check for similarities between subjects through a hierarchical analysis that allowed three different gait patterns to be identified, most of the differences corresponding to the frontal and transverse planes.

Prevalence of Joint Gait Patterns Defined by a Delphi Consensus Study Is Related to Gross Motor Function, Topographical Classification, Weakness, and Spasticity, in Children with Cerebral Palsy

During a Delphi consensus study, a new joint gait classification system was developed for children with cerebral palsy (CP). This system, whose reliability and content validity have previously been established, identified 49 distinct joint patterns. The present study aims to provide a first insight toward the construct validity and clinical relevance of this classification system. The retrospective sample of convenience consisted of 286 patients with spastic CP (3-18 years old, GMFCS levels I-III, 166 with bilateral CP). Kinematic and kinetic trials from three-dimensional gait analysis were classified according to the definitions of the Delphi study, and one classified trial was randomly selected for each included limb (n = 446). Muscle weakness and spasticity were assessed for different muscle groups acting around the hip, knee, and ankle. Subsequently, Pearson Chi square tests, Cramer's V, and adjusted standardized residuals were calculated to explore the strength and direction of the associations between the joint patterns, and the different patient-specific characteristics (i.e., age, GMFCS level, and topographical classification) or clinical symptoms (muscle weakness and spasticity). Patient-specific characteristics showed several significant associations with the patterns of different joints, but the strength of most identified associations was weak. Apart from the knee during stance phase and the pelvis in the sagittal plane, the results systematically showed that the patterns with "minor gait deviations" were the most frequently observed. These minor deviations were found significantly more often in limbs with a lower level of spasticity and good muscle strength. Several other pathological joint patterns were moderately associated with weakness or spasticity, including but not limited to "outtoeing" for weakness and "intoeing" for spasticity. For the joints in the sagittal plane, significantly stronger associations were found with muscle weakness and spasticity, possibly because most of the evaluated muscles in this study mainly perform sagittal plane motions. Remarkably, the hip patterns in the coronal plane did not associate significantly with any of the investigated variables. Although further validation is warranted, this study contributes to the construct validity of the joint patterns of the Delphi consensus study, by demonstrating their ability to distinguish between clinically relevant subgroups in CP.

Inertial Sensor-Based Gait Recognition: A Review

With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

Gait pattern classification in children with Charcot-Marie-Tooth disease type 1A

Gait pattern classification may assist in clinical decision making and cluster analysis (CA) has been often adopted to this aim. The goal of this study was to identify, through CA, typical walking patterns in a group of 21 young subjects with CMT1A, a hereditary progressive neuropathy, and to study possible correlation with the disease's clinical status. The protocol included kinematic/kinetic analysis of natural walking and more demanding locomotor tasks, i.e. toe- and heel-walking. Hierarchical cluster analysis was carried out on parameters related to primary signs (foot-drop and push-off deficit) and, separately, to compensatory mechanisms at proximal (pelvis, hip and knee) or distal (ankle) level. CA on primary signs during natural walking identified three clusters: (1) pseudo-normal patients (PN), not significantly different from controls; (2) patients showing only foot-drop (FD); (3) patients with foot-drop and push-off deficit (FD&POD). Patients belonging to the PN subgroup showed distal abnormalities during heel-walking. The FD&POD subgroup was associated to a significantly worse clinical score (CMTES, p<0.05). The main compensatory strategies, which occurred independently from primary clusterization, included augmented hip/knee flexion in swing (steppage) and early ankle plantarflexion at mid stance (vaulting). We concluded that, although a number of young CMT1A patients do not show typical primary deviations during natural walking, they do show significant abnormalities in more demanding locomotor tasks that should be therefore considered. It is also hypothesized that progression of this degenerative condition may be associated to the migration of patients to more severe clusters, with possible appearance of compensatory strategies.