Remote Pathological Gait Classification System

Abstract

Several pathologies can alter the way people walk, i.e. their gait. Gait analysis can therefore be used to detect impairments and help diagnose illnesses and assess patient recovery. Using vision-based systems, diagnoses could be done at home or in a clinic, with the needed computation being done remotely. State-of-the-art vision-based gait analysis systems use deep learning, requiring large datasets for training. However, to our best knowledge, the biggest publicly available pathological gait dataset contains only 10 subjects, simulating 4 gait pathologies. This paper presents a new dataset called GAIT-IT, captured from 21 subjects simulating 4 gait pathologies, with 2 severity levels, besides normal gait, being considerably larger than publicly available gait pathology datasets, allowing to train a deep learning model for gait pathology classification. Moreover, it was recorded in a professional studio, making it possible to obtain nearly perfect silhouettes, free of segmentation errors. Recognizing the importance of remote healthcare, this paper proposes a prototype of a web application allowing to upload a walking person's video, possibly acquired using a smartphone camera, and execute a web service that classifies the person's gait as normal or across different pathologies. The web application has a user friendly interface and could be used by healthcare professionals or other end users. An automatic gait analysis system is also developed and integrated with the web application for pathology classification. Compared to state-of-the-art solutions, it achieves a drastic reduction in the number of model parameters, which means significantly lower memory requirements, as well as lower training and execution times. Classification accuracy is on par with the state-of-the-art.

A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data

“…Although there was a strong correlation between the clinician labels and the model estimates, the classifiers implemented were trained using data that had availability restrictions; such limitations are an obstacle to other studies that have had to rely on significantly smaller data sets to carry out their analysis. For instance, in [46], the study consisted of 37 patients, while in [47], the proposed system used the data from 23 patients with Parkinson's disease, a number comparable to that of participants in [48].…”

“…This paper provides an alternative to the these methods by creating additional information based on real, pre-existing data and implementing it into a practical pose-estimation algorithm. Although a significant amount of work remains for classification stages through deeplearning technologies to be used in the clinical field on a regular basis, our process of synthetic generation is a useful tool that can satisfy the need for acquiring larger data sets suggested by previous studies [21,30,48,52] through sensor-less technology.…”