The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-09, Vol.24 (18), p.5957
• Main Authors: Franco, Alessandra, Russo, Michela, Amboni, Marianna, Ponsiglione, Alfonso Maria, Di Filippo, Federico, Romano, Maria, Amato, Francesco, Ricciardi, Carlo
• Format: Article
• Language: English
• Subjects: Cameras; Decision-making; Deep Learning; Development and progression; Diseases; Gait; Gait - physiology; gait analysis; Gait Analysis - methods; Human mechanics; human pose estimation; Humans; Movement disorders; Neural networks; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson’s disease; Quality of Life; Review; Sensors; Smartphones; Systematic review; United Kingdom; video motion capture; Wearable Electronic Devices; wearable sensors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24185957

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.