Using transfer learning for classification of gait pathologies

Different diseases can affect an individual’s gait in different ways and, therefore, gait analysis can provide important insights into an individual’s health and well-being. Currently, most systems that perform gait analysis using 2D video are limited to simple binary classification of gait as being either normal or impaired. While some systems do perform gait classification across different pathologies, the reported results still have a considerable margin for improvement. This paper presents a novel system that performs classification of gait across different pathologies, with considerably improved results. The system computes the walking individual’s silhouettes, which are computed from a 2D video sequence, and combines them into a representation known as the gait energy image (GEI), which provides robustness against silhouette segmentation errors. In this work, instead of using a set of handcrafted gait features, feature extraction is done using the VGG-19 convolutional neural network. The network is fine-tuned to automatically extract the features that best represent gait pathologies, using transfer learning. The use of transfer learning improves the classification accuracy while avoiding the need of a very large training set, as the network is pre-trained for generic image description, which also contributes to a better generalization when tested across different datasets. The proposed system performs the final classification using linear discriminant analysis (LDA). Obtained results show that the proposed system outperforms the state-of-the-art, achieving a classification accuracy of 95% on a dataset containing gait sequences affected by diplegia, hemiplegia, neuropathy and Parkinson’s disease, along with normal gait sequences.info:eu-repo/semantics/acceptedVersio

Estimation and validation of temporal gait features using a markerless 2D video system

Background and Objective: Estimation of temporal gait features, such as stance time, swing time and gait cycle time, can be used for clinical evaluations of various patient groups having gait pathologies, such as Parkinson’s diseases, neuropathy, hemiplegia and diplegia. Most clinical laboratories employ an optoelectronic motion capture system to acquire such features. However, the operation of these systems requires specially trained operators, a controlled environment and attaching reflective markers to the patient’s body. To allow the estimation of the same features in a daily life setting, this paper presents a novel vision based system whose operation does not require the presence of skilled technicians or markers and uses a single 2D camera. Method: The proposed system takes as input a 2D video, computes the silhouettes of the walking person, and then estimates key biomedical gait indicators, such as the initial foot contact with the ground and the toe off instants, from which several other temporal gait features can be derived. Results: The proposed system is tested on two datasets: (i) a public gait dataset made available by CASIA, which contains 20 users, with 4 sequences per user; and (ii) a dataset acquired simultaneously by a marker-based optoelectronic motion capture system and a simple 2D video camera, containing 10 users, with 5 sequences per user. For the CASIA gait dataset A the relevant temporal biomedical gait indicators were manually annotated, and the proposed automated video analysis system achieved an accuracy of 99% on their identification. It was able to obtain accurate estimations even on segmented silhouettes where, the state-of-the-art markerless 2D video based systems fail. For the second database, the temporal features obtained by the proposed system achieved an average intra-class correlation coefficient of 0.86, when compared to the "gold standard" optoelectronic motion capture system. Conclusions: The proposed markerless 2D video based system can be used to evaluate patients’ gait without requiring the usage of complex laboratory settings and without the need for physical attachment of sensors/markers to the patients. The good accuracy of the results obtained suggests that the proposed system can be used as an alternative to the optoelectronic motion capture system in non-laboratory environments, which can be enable more regular clinical evaluations.info:eu-repo/semantics/acceptedVersio

Gait recognition in the wild using shadow silhouettes

Gait recognition systems allow identification of users relying on features acquired from their body movement while walking. This paper discusses the main factors affecting the gait features that can be acquired from a 2D video sequence, proposing a taxonomy to classify them across four dimensions. It also explores the possibility of obtaining users’ gait features from the shadow silhouettes by proposing a novel gait recognition system. The system includes novel methods for: (i) shadow segmentation, (ii) walking direction identification, and (iii) shadow silhouette rectification. The shadow segmentation is performed by fitting a line through the feet positions of the user obtained from the gait texture image (GTI). The direction of the fitted line is then used to identify the walking direction of the user. Finally, the shadow silhouettes thus obtained are rectified to compensate for the distortions and deformations resulting from the acquisition setup, using the proposed four-point correspondence method. The paper additionally presents a new database, consisting of 21 users moving along two walking directions, to test the proposed gait recognition system. Results show that the performance of the proposed system is equivalent to that of the state-of-the-art in a constrained setting, but performing equivalently well in the wild, where most state-of-the-art methods fail. The results also highlight the advantages of using rectified shadow silhouettes over body silhouettes under certain conditions.info:eu-repo/semantics/acceptedVersio

Anomaly gait detection in ASD children based on markerless-based gait features

Children with autism are known for their difficulties in social interaction, communication, and behaviour characteristics. Hence, this study proposed to develop a markerless-based gait method for anomaly gait detection in children with autism spectrum disorder (ASD). Firstly, a depth sensor is used during walking gait data collection of the 23 ASD children and 30 typical healthy developing (TD) children. Further, these walking gait data are divided into the Reference Joint (REF) and Direct Joint (DIR) features. For each type, five sets of features are derived that represents the whole body, upper body, lower body, the right and left side of the body. The three classifiers used to validate the effectiveness of the proposed method are Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Results showed that the highest accuracy, precisely 94.22%, is achieved using the ANN classifier with DIR1 gait features representing the whole body. The highest sensitivity and specificity obtained are 94.49% and 93.93% accordingly. In addition, the proposed markerless model using the DIR1 gait features and the ANN as classifier also outperformed previous studies that have utilised the marker-based model. This promising result showed that the proposed method could be used for early intervention for the ASD group. The markerless-based gait technique also has fewer experiment protocols, thus causing the ASD children to feel more comfortable

A vision based proposal for classification of normal and abnormal gait using RGB camera

Human gait is mainly related to the foot and leg movements but, obviously, the entire motor system of the human body is involved. We hypothesise that movement parameters such as dynamic balance, movement harmony of each body element (arms, head, thorax…) could enable us to finely characterise gait singularities to pinpoint potential diseases or abnormalities in advance. Since this paper deals with the preliminary problem pertaining to the classification of normal and abnormal gait, our study will revolve around the lower part of the body. Our proposal presents a functional specification of gait in which only observational kinematic aspects are discussed. The resultant specification will confidently be open enough to be applied to a variety of gait analysis problems encountered in areas connected to rehabilitation, sports, children’s motor skills, and so on. To carry out our functional specification, we develop an extraction system through which we analyse image sequences to identify gait features. Our prototype not only readily lets us determine the dynamic parameters (heel strike, toe off, stride length and time) and some skeleton joints but also satisfactorily supplies us with a proper distinction between normal and abnormal gait. We have performed experiments on a dataset of 30 samples.This research is part of the FRASE MINECO project (TIN2013-47152-C3-2-R) funded by the Ministry of Economy and Competitiveness of Spain