Automated feet detection for clinical gait assessment

Computer Methods and Programs in Biomedicine

Vroey

Claeys

et al. 2019

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.

Section: Accepted Manuscriptmentioning

confidence: 99%

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

Computer Methods and Programs in Biomedicine

Vroey

Claeys

et al. 2019

“…Since the major articulations during a gait cycle occur in the sagittal plane [12], some vision based systems rely on a single side view video sequence of an individual to perform gait analysis. Such systems typically acquire several biomechanical features, such as step length, leg angles, gait cycle time [13], cadence, speed, and stride length [14], or the fraction of the stance and swing phases during a gait cycle [15], using the available side view body silhouettes. These features are then used to classify gait as being either normal or impaired.…”

Section: A State-of-the-artmentioning

confidence: 99%

Using transfer learning for classification of gait pathologies

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Lobat

Soares

2018

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.

“…For these reasons, the work presented in this paper focuses on a markerless approach, adequate for a simple and cost-effective setup, relying on a single RGB video camera [ 14 , 15 , 16 ], as opposed to other markerless setups, which include the usage of two or more RGB video cameras [ 17 ] or cameras equipped with depth sensors [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…Appearance-based approaches produce gait representations that do not contain any prior knowledge of human motion (e.g., binary silhouettes), but from which gait features can be extracted to analyze a person’s gait. Examples of such features include cadence and speed [ 14 ], step length, stance phase, and swing phase lengths [ 15 ], or biomechanical features such as center of gravity shifts, torso orientation, and the fraction of the cycle during which a foot is flat on the ground (i.e., flat foot fraction) [ 21 ]. A widely used gait representation for acquiring gait features is the Gait Energy Image (GEI) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Albuquerque

Correia

et al. 2021

Sensors

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.