Classification of Ataxic Gait.
Ontology highlight
ABSTRACT:
SUBMITTER: Vysata O
PROVIDER: S-EPMC8402252 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ataxia as the main complaint in a young patient prompts to diagnose neurodegenerative diseases. Not all ataxias are irreversible. We present a video of a patient in the clinic with ataxia diagnosed as subacute combined degeneration of the cord.
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Project description:Introduction and objectivesProgressive ataxic gait is a common symptom in individuals with Familial Dysautonomia (FD). At least 50% of adults with FD require assistance with walking. Our aims were to describe the medical condition of individuals with FD (ii) compare their gait characteristics to healthy individuals, and (iii) assess correlations between gait measures, presence of unstable gait pattern and frequency of falls.MethodsTwelve subjects with FD (7 males, age 25.3±10.6 years) and 16 healthy participants (6 males, age 35.9±11.9 years) were recruited. Gait kinematics, gait symmetry, dynamic muscle activity, and foot deep vibration sensation were recorded.ResultsAtaxic gait degrees were: severe (6 out of 12), moderate (4 out of 12) and low (2 out of 12). The number of falls correlated with base width asymmetry. Crouch gait was noted in 3 out of 12 of the subjects.ConclusionsIn-depth quantitative gait analysis of individuals with FD revealed ataxic gait. The ataxic pattern might be a result of combined neurological deficiencies and osseous deformities. Increasing the base of support of patients with FD might increase the symmetry of the base width during gait and decrease the number of falls. Additionally, perturbation treatment and dynamic balance exercises may be recommended in order to improve compensatory strategies. Future investigation of this population should include quantification of osseous rotations of the lower limb in order to fully understand its effect on their gait pattern and falls.
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Project description:Background:While accumulating evidence suggests that balance and gait impairments are commonly seen in patients with essential tremor (ET), questions remain regarding their prevalence, their relationship with normal aging, whether they are similar to the impairments seen in spinocerebellar ataxias, their functional consequences, and whether some ET patients carry greater susceptibility. Methods:We conducted a literature search (until December 2018) on this topic. Results:We identified 23 articles on gait or balance impairments in ET. The prevalence of balance impairment (missteps on tandem walk test) was seven times higher in ET patients than controls. Gait impairments in ET included reduced speed, increased asymmetry, and impaired dynamic balance. While balance and gait problems worsened with age, ET patients were more impaired than controls, independent of age. The pattern of impairments seen in ET was qualitatively similar to that seen in spinocerebellar ataxias. Balance and gait impairments resulted in greater number of near falls in ET patients. Factors associated with balance and gait impairments in ET included age, presence of tremor in midline structures, and cognitive dysfunction. Discussion:Accumulating evidence suggests that balance and gait impairments are common in ET patients and occur to a greater extent in controls. Thus, they represent a disease-associated feature. These impairments, which are qualitatively similar to those seen in spinocerebellar ataxias, are not merely subclinical but result in difficulty performing functional tasks and increase falls risk. A subset of patients is more susceptible to balance and gait impairments. The full spectrum of impairments remains to be characterized.
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Project description:Ataxia is an impairment of the coordination of movement or the interaction of associated muscles, accompanied by a disturbance of the gait pattern. Diagnosis of this clinical sign, and evaluation of its severity is usually done using subjective scales during neurological examination. In this exploratory study we investigated if inertial sensors in a smart phone (3 axes of accelerometer and 3 axes of gyroscope) can be used to detect ataxia. The setting involved inertial sensor data collected by smartphone placed on the dog's back while walking in a straight line. A total of 770 walking sessions were evaluated comparing the gait of 55 healthy dogs to the one of 23 dogs with ataxia. Different machine learning techniques were used with the K-nearest neighbors technique reaching 95% accuracy in discriminating between a healthy control group and ataxic dogs, indicating potential use for smartphone apps for canine ataxia diagnosis and monitoring of treatment effect.
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Project description:Patients and rodents with cerebellar damage display ataxic gaits characterized by impaired coordination of limb movements. Here, gait ataxia in mice with a null mutation of the gene for the cerebellin 1 precursor protein (cbln1-null mice) was investigated by kinematic analysis of hindlimb movements during locomotion. The Cbln1 protein is predominately produced and secreted from cerebellar granule cells. The cerebellum of cbln1-null mice is characterized by an 80% reduction in the number of parallel fiber-Purkinje cell synapses compared with wild-type mice. Our analyses identified prominent differences in the temporal parameters of locomotion between cbln1-null and wild-type mice. The cbln1-null mice displayed abnormal hindlimb movements that were characterized by excessive toe elevation during the swing phase, and by severe hyperflexion of the ankles and knees. When recombinant Cbln1 protein was injected into the cerebellum of cbln1-null mice, the step cycle and stance phase durations increased toward those of wild-type mice, and the angular excursions of the knee during a cycle period showed a much closer agreement with those of wild-type mice. These findings suggest that dysfunction of the parallel fiber-Purkinje cell synapses might underlie the impairment of hindlimb movements during locomotion in cbln1-null mice.
| S-EPMC5906462 | biostudies-literature
Project description:PurposeThe classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA) is replaced by an iterative application of the support vector machine (SVM). The aim was to use classifyability instead of variability to build a subspace (SVM space) that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA) in the SVM space.MethodsEleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohen's d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant.ResultsThe SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space.ConclusionsA combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.
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Project description:ObjectiveTo develop an objective and efficient method to automatically identify Parkinson's disease (PD) and healthy control (HC).MethodsWe design a novel model based on residual network (ResNet) architecture, named PD-ResNet, to learn the gait differences between PD and HC and between PD with different severity levels. Specifically, a polynomial elevated dimensions technique is applied to increase the dimensions of the input gait features; then, the processed data is transformed into a 3-dimensional picture as the input of PD-ResNet. The synthetic minority over-sampling technique (SMOTE), data augmentation, and early stopping technologies are adopted to improve the generalization ability. To further enhance the classification performance, a new loss function, named improved focal loss function, is developed to focus on the train of PD-ResNet on the hard samples and to discard the abnormal samples.ResultsThe experiments on the clinical gait dataset show that our proposed model achieves excellent performance with an accuracy of 95.51%, a precision of 94.44%, a recall of 96.59%, a specificity of 94.44%, and an F1-score of 95.50%. Moreover, the accuracy, precision, recall, specificity, and F1-score for the classification of early PD and HC are 92.03%, 94.20%, 90.28%, 93.94%, and 92.20%, respectively. Furthermore, the accuracy, precision, recall, specificity, and F1-score for the classification of PD with different severity levels are 92.03%, 94.29%, 90.41%, 93.85%, and 92.31%, respectively.ConclusionOur proposed method shows better performance than the traditional machine learning and deep learning methods.Clinical impactThe experimental results show that the proposed method is clinically meaningful for the objective assessment of gait motor impairment for PD patients.
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Project description: Not available
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Project description:Early detection of Alzheimer's Disease and Related Disorders (ADRD) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task was administered at two different inter-stimulus intervals representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated Measures ANOVA showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single vs. Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single vs. Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.
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Project description: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.
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