EXPLOITING VISUAL CUES FOR LEARNING GAIT PATTERNS ASSOCIATED WITH NEUROLOGICAL DISORDERS

2017 ◽  
Vol 79 (3) ◽  
Author(s):  
Kuhelee Roy ◽  
Geelapaturu Subrahmanya Venkata Radha Krish Rao ◽  
Savarimuthu, Margret Anouncia
Keyword(s):  
Early Diagnosis ◽  
Real Time ◽  
Neurological Disorders ◽  
Visual Cues ◽  
Gait Pattern ◽  
Shape Features ◽  
Gait Patterns ◽  
Normal Gait ◽  
Abnormal Gait ◽  
Gait Abnormalities

Records of cases involving neurological disorders often exhibit abnormalities in the gait pattern of an individual. As mentioned in various articles, the causes of various gait disorders can be attributed to neurological disorders. Hence analysis of gait abnormalities can be a key to predict the type of neurological disorders as a part of early diagnosis. A number of sensor-based measurements have aided towards quantifying the degree of abnormalities in a gait pattern. A shape oriented motion based approach has been proposed in this paper to envisage the task of classifying an abnormal gait pattern into one of the five types of gait viz. Parkinsonian, Scissor, Spastic, Steppage and Normal gait. The motion and shape features for two cases viz. right-leg-front and left-leg-front will be taken into account. Experimental results of application on real-time videos suggest the reliability of the proposed method.

The validity of video clips in the diagnosis of gait disorder

2007 ◽  
Vol 13 (7) ◽  
pp. 333-336 ◽  
Author(s):  
Salih A Salih ◽  
Richard Wootton ◽  
Elaine Beller ◽  
Len Gray
Keyword(s):  
Gold Standard ◽  
Video Clip ◽  
Clinical Documentation ◽  
Gait Disorder ◽  
Average Sensitivity ◽  
Gait Patterns ◽  
Video Clips ◽  
Normal Gait ◽  
Abnormal Gait ◽  
Gait Abnormalities

We investigated the accuracy and validity of clinical gait assessment, performed by experienced geriatricians viewing video clips of 10 s duration. Nineteen patients with normal or characteristic abnormal gait patterns were studied. The treating physician's diagnosis served as the gold standard. Another live assessment was then performed by a geriatrician blinded to the medical record to establish inter-rater reliability of live assessments. Subsequently, each gait video clip was examined by two independent geriatricians without any background clinical documentation. Diagnostic accuracy was tested at two levels – whether the gait was abnormal, and the specific gait diagnosis. The agreement of the video clip examination with the gold standard to identify abnormal gait from normal gait ranged from substantial to excellent among assessors ( κ = 0.68–0.85), although low agreement with the gold standard was achieved in the detection of specific gait diagnosis (average agreement between both viewing geriatricians 50%). The technique appears to be a valid screening procedure for detecting gait abnormalities (average sensitivity 100%, specificity 70%).

Estimates of Gastrocnemius Muscle Length During Simulated Pathological Gait

2013 ◽  
Vol 29 (2) ◽  
pp. 127-134 ◽  
Author(s):  
Smita Rao ◽  
Fred Dietz ◽  
H. John Yack
Keyword(s):  
Gastrocnemius Muscle ◽  
Muscle Length ◽  
Gait Pattern ◽  
Line Model ◽  
Gait Patterns ◽  
Segmented Model ◽  
Normal Gait ◽  
Straight Line ◽  
Crouch Gait ◽  
Pathological Gait

The purpose of this study was to compare estimates of gastrocnemius muscle length (GML) obtained using a segmented versus straight-line model in children. Kinematic data were acquired on eleven typically developing children as they walked under the following conditions: normal gait, crouch gait, equinus gait, and crouch with equinus gait. Maximum and minimum GML, and GML change were calculated using two models: straight-line and segmented. A two-way RMANOVA was used to compare GML characteristics. Results indicated that maximum GML and GML change during simulated pathological gait patterns were influenced by model used to calculate gastrocnemius muscle length (interaction: P = .004 and P = .026). Maximum GML was lower in the simulated gait patterns compared with normal gait (P < .001). Maximum GML was higher with the segmented model compared with the straight-line model (P = .030). Using either model, GML change in equinus gait and crouch with equinus gait was lower compared with normal gait (P < .001). Overall, minimum GML estimated with the segmented model was higher compared with the straight-line model (P < .01). The key findings of our study indicate that GML is significantly affected by both gait pattern and method of estimation. The GML estimates tended to be lower with the straight-line model versus the segmented model.

scholarly journals GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mohammad Farukh Hashmi ◽  
B. Kiran Kumar Ashish ◽  
Prabhu Chaitanya ◽  
Avinash Keskar ◽  
Sinan Q. Salih ◽  
...  
Keyword(s):  
Real Time ◽  
Gait Pattern ◽  
Foot Pressure ◽  
Gait Patterns ◽  
Softmax Classifier ◽  
Gait Parameters ◽  
Unique Identity ◽  
Access Authentication ◽  
Deep Convolution Network ◽  
Hierarchical Features

Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90 % ± 1.3 % in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.

scholarly journals Real-Time Auditory Feedback–Induced Adaptation to Walking Among Seniors Using the Heel2Toe Sensor: Proof-of-Concept Study

10.2196/13889 ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. e13889
Author(s):  
Kedar KV Mate ◽  
Ahmed Abou-Sharkh ◽  
José A Morais ◽  
Nancy E Mayo
Keyword(s):  
Real Time ◽  
Auditory Feedback ◽  
Visual Cues ◽  
Gait Training ◽  
Gait Pattern ◽  
Arm Swing ◽  
Simple Strategy ◽  
Minute Period ◽  
Gait Parameters ◽  
Strategy Changes

Background Evidence shows that gait training in older adults is effective in improving the gait pattern, but the effects abate with cessation of training. During gait training, therapists use a number of verbal and visual cues to place the heel first when stepping. This simple strategy changes posture from stooped to upright, lengthens the stride, stimulates pelvic and trunk rotation, and facilitates arm swing. These principles guided the development of the Heel2Toe sensor that provides real-time auditory feedback for each good step, in which the heel strikes first. Objective This feasibility study aimed (1) to contribute evidence toward the feasibility and efficacy potential for home use of the Heel2Toe sensor that provides real-time feedback and (2) to estimate changes in gait parameters after five training sessions using the sensor. Methods A pre-post study included 5 training sessions over 2 weeks in the community on a purposive sample of six seniors. Proportion of good steps, angular velocity (AV) at each step, and cadence over a 2- minute period were assessed as was usability and experience. Results All gait parameters, proportion of good steps, AV, and duration of walking bouts improved. The coefficient of variation of AV decreased, indicating consistency of stepping. Conclusions Efficacy potential and feasibility of the Heel2Toe sensor were demonstrated.

scholarly journals Real-Time Auditory Feedback–Induced Adaptation to Walking Among Seniors Using the Heel2Toe Sensor: Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Kedar K.V. Mate ◽  
Ahmed Abou-Sharkh ◽  
José A. Morais ◽  
Nancy E. Mayo
Keyword(s):  
Real Time ◽  
Auditory Feedback ◽  
Visual Cues ◽  
Gait Training ◽  
Gait Pattern ◽  
Arm Swing ◽  
Simple Strategy ◽  
Minute Period ◽  
Gait Parameters ◽  
Strategy Changes

BACKGROUND Evidence shows that gait training in older adults is effective in improving the gait pattern, but the effects abate with cessation of training. During gait training, therapists use a number of verbal and visual cues to place the heel first when stepping. This simple strategy changes posture from stooped to upright, lengthens the stride, stimulates pelvic and trunk rotation, and facilitates arm swing. These principles guided the development of the Heel2Toe sensor that provides real-time auditory feedback for each good step, in which the heel strikes first. OBJECTIVE This feasibility study aimed (1) to contribute evidence toward the feasibility and efficacy potential for home use of the Heel2Toe sensor that provides real-time feedback and (2) to estimate changes in gait parameters after five training sessions using the sensor. METHODS A pre-post study included 5 training sessions over 2 weeks in the community on a purposive sample of six seniors. Proportion of good steps, angular velocity (AV) at each step, and cadence over a 2- minute period were assessed as was usability and experience. RESULTS All gait parameters, proportion of good steps, AV, and duration of walking bouts improved. The coefficient of variation of AV decreased, indicating consistency of stepping. CONCLUSIONS Efficacy potential and feasibility of the Heel2Toe sensor were demonstrated.

scholarly journals Three-Dimensional Human Gait Pattern: Reference Data for Young, Active Women Walking with Low, Preferred, and High Speeds

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Slawomir Winiarski ◽  
Jadwiga Pietraszewska ◽  
Bogdan Pietraszewski
Keyword(s):  
Reference Data ◽  
Three Dimensional ◽  
Gait Pattern ◽  
Human Gait ◽  
Specific Population ◽  
Gait Patterns ◽  
Normative Database ◽  
High Speeds ◽  
Normal Gait ◽  
Adjustment Of Parameters

Normal gait pattern is the key component in the investigation of pathological gait patterns. In computer motion analysis there is a need to include data from participants with different somatic structures to develop a normative database or to limit the database results to a specific population. The aim of this study was to determine kinematic gait patterns for young, active women walking with low, preferred, and self-selected speeds with regard to their somatic characteristics. Laboratory-based gait analysis was performed on 1320 gait cycles taken from 20 young, active women walking with three different speeds. Comprehensive anthropometric measurements and descriptive statistics were used to describe spatiotemporal and angular variables at each walking condition. The results demonstrated some significant differences in young, active women walking between different speeds and compared to the literature. This suggests that there is a need to include data from participants with different somatic structures to develop a normative database or limit the database results to a specific population. Detailed linear and angular kinematic variables allow for proper adjustment of parameters depending on the gait speed of people with locomotion disorders.

scholarly journals Adjustable Method for Real-Time Gait Pattern Detection Based on Ground Reaction Forces Using Force Sensitive Resistors and Statistical Analysis of Constant False Alarm Rate

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3764 ◽  
Author(s):  
Fangli Yu ◽  
Jianbin Zheng ◽  
Lie Yu ◽  
Rui Zhang ◽  
Hailin He ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Sliding Window ◽  
Gait Pattern ◽  
Contact Forces ◽  
Pattern Detection ◽  
Constant False Alarm Rate ◽  
Gait Patterns ◽  
Reference Methods

A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. To overcome these drawbacks, this study utilized the constant false alarm rate (CFAR) to analyze the characteristics of GCF signals. Specifically, a sliding window detector is built to record the lasting time of the curvature of the GCF signals and one complete gait cycle could be divided into three areas, such as continuous ascending area, continuous descending area and unstable area. Then, the GCF values in the unstable area are used to compute a threshold through the CFAR. Finally, the new gait pattern detection rules are proposed which include the results of the sliding window detector and the division results through the computed threshold. To verify this idea, a data acquisition board is designed to collect the GCF data from able-bodied subjects. Meanwhile, in order to test the reliability of the proposed method, five threshold-based methods in the literature are introduced as reference methods and the reliability is validated by comparing the detection results of the proposed method with those of the reference methods. Experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively and obtain high reliabilities compared with the reference methods.

scholarly journals Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1864
Author(s):  
Fu-Cheng Wang ◽  
Szu-Fu Chen ◽  
Chin-Hsien Lin ◽  
Chih-Jen Shih ◽  
Ang-Chieh Lin ◽  
...  
Keyword(s):  
Stroke Patients ◽  
Gait Patterns ◽  
Gait Abnormality ◽  
Abnormal Gait ◽  
Average Accuracy ◽  
Targeted Treatments ◽  
Measurement Units ◽  
Gait Abnormalities ◽  
Rehabilitation Strategies

This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.

Gait abnormalities and inhibitive casts in cerebral palsy. Literature review

1989 ◽  
Vol 79 (2) ◽  
pp. 53-59 ◽  
Author(s):  
CJ Hanson ◽  
LJ Jones
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Cerebral Palsy ◽  
Gait Pattern ◽  
Spastic Cerebral Palsy ◽  
Nervous System Disorder ◽  
Central Nervous System Disorder ◽  
Normal Gait ◽  
Gait Abnormalities ◽  
System Disorder

Although cerebral palsy is primarily a central nervous system disorder, its major manifestations are musculoskeletal. The authors focus on normal tonic reflexes of the foot and the developmental consequences of failure to inhibit these reflexes. Various gait abnormalities seen in cerebral palsy are reviewed. Finally, the use of inhibitive casts as a conservative modality for treating hyperactive reflexes in the spastic cerebral palsy child is discussed. The reduction of abnormal tone facilitates the development of a normal gait pattern in these children.

scholarly journals Gait Training in Orthopedic Rehabilitation after Joint Replacement - Back to Normal Gait with Sonification?

2019 ◽  
Vol 18 (2) ◽  
pp. 34-48 ◽  
Author(s):  
J. Pietschmann ◽  
F. Geu Flores ◽  
T. Jöllenbeck
Keyword(s):  
New Technologies ◽  
Gait Training ◽  
Gait Pattern ◽  
Contralateral Side ◽  
Angle Measurement ◽  
Gait Patterns ◽  
Normal Gait ◽  
Frequent Use ◽  
Orthopedic Rehabilitation ◽  
Post Test

Abstract Even several years after total hip (THR) and total knee replacement (TKR) surgery patients frequently show deficient gait patterns leading to overloads and relieving postures on the contralateral side or in the spine. Gait training is, in these cases, an essential part of rehabilitation. The aim of this study was to compare different feedback methods during gait training after THR and TKR focusing, in particular, on auditory feedback via sonification. A total of 240 patients after THR and TKR were tested in a pre-post-test design during a 3-week rehabilitation period. Even though sonification did not show, statistically, a clear advantage over other feedback methods, it was well accepted by the patients and seemed to significantly change gait pattern during training. A sudden absence of sonification during training led to a rapid relapse into previous movement patterns, which highlights its effectiveness in breaking highly automated gait patterns. A frequent use of sonification during and after rehabilitation could, hence, reduce overloading after THR and TKR. This may soon be viable, since new technologies, such as inertial measurement units, allow for wearable joint angle measurement devices. Back to normal gait with sonification seems possible.

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