The aim of this study was to determine which supervised machine learning (ML) algorithm
can most accurately classify people with Parkinson's disease (pwPD) from speed-matched …

In skeleton-based abnormal gait recognition, using original skeleton data decreases the
recognition performance because they contain noise and irrelevant information. Instead of …

Computer vision-based clinical gait analysis is the subject of permanent research. However,
there are very few datasets publicly available; hence the comparison of existing methods …

As depression becomes more commonplace in society, the timely and effective detection of
the signs of depression for its prevention and early treatment becomes more important. Gait …

With the development of depth sensors and skeleton tracking algorithms, many skeleton-
based pathological gait classification methods have recently been proposed. However …

Classification of pathological gaits has an important role in finding a weakened body part
and diagnosing a disease. Many machine learning-based approaches have been proposed …

A self-managed, home-based system for the automated assessment of a selected set of
Parkinson's disease motor symptoms is presented. The system makes use of an optical RGB …

In this work, we propose an end-to-end deep learning model that uses the skeleton data
recorded by Kinect to capture spatiotemporal patterns for gait anomaly recognition. Via …

Parkinson's disease (PD) lacks a definitive diagnosis, with the observation of motion
patterns being the main method of characterizing disease progression and planning patient …

Osteoarthritis (OA) is the most common type of joint-related diseases, which affects millions
of people worldwide. Expensive and time-consuming medical imaging can provide precise …