Multi-speed transformer network for neurodegenerative disease assessment and activity recognition.
Journal:
Computer methods and programs in biomedicine
PMID:
36706617
Abstract
BACKGROUND AND OBJECTIVE: Neurodegenerative diseases are the most frequent age-related diseases. This type of disease, if not discovered in the initial stage, will compromise the quality of life of the affected subject. Thus, a timely diagnosis is of paramount importance. One of the most used tasks from neurologists to detect and determine the severity of the disease is analysing human gait. This work presents the dataset named "Beside Gait" containing timeseries of coordinates of extracted body joints of people with neurodegenerative diseases in various stages of the disease as well as control subjects. In addition, the novel Multi-Speed transformer technique will be presented and benchmarked against several other techniques making use of deep learning and Shallow Learning. The objective is to recognize subjects affected by some form of neurodegenerative disease in early stage using a computer vision technique making use of deep learning that can be integrated into a smartphone app for offline inference with the aim of promptly initiate investigations and treatment to improve the patient's quality of life.