Abstract
Parkinson's Disease (PD) being the second most hazardous neurological disorder has developed its roots in damaging people's quality of life (QOL). The ineffectiveness of clinical rating scales makes the PD diagnosis a very complicated task. Thus, more efficient systems are required to perform an automated evaluation of PD for its earlier detection and to enhance life expectancy rate. Gait based clinical diagnosis can provide useful indications regarding the presence of PD. From recent years, computer vision-based (VB) analysis is in great demand and seems to be highly effective in PD inspection. The objective of this article is to systematically analyze the applications of computer vision in PD evaluation through gait. This paper surveys the VB PD gait acquisition modalities as well as provides a concise overview of preprocessing techniques. The study presents a description of PD related gait features, extraction and selection methods used for PD analysis. A number of machine learning techniques for classification of PD and healthy gait are also discussed. This article extensively surveys PD gait datasets considering data from 1997 to 2018. Also, several research gaps in existing studies have identified that need to be addressed in the future. At last, an outline of the proposed idea is given that can cope up with the related issues and can lead to quality VB PD gait investigation.
Highlights
In the present era of remarkable technological advancements, identification of an abnormal health condition is of highest concern
In recent years, the number of people suffering from Parkinson’s Disease (PD) has increased substantially and is recorded as the most deadly health issue worldwide
This article provides an exhaustive survey of existing research work concerning vision-based PD diagnosis through gait from 2005 to Feb. 2019
Summary
In the present era of remarkable technological advancements, identification of an abnormal health condition is of highest concern. In spite of having the huge capability of sensors technology to perform direct measurements of PD gait, it suffers from certain drawbacks such as the requirement of high cost, time, power and technical skills, wearing discomfort, drifting effect problem etc.[3], [12], [13], [48] In this survey article, further SB modality is not explored and reviewed in detail. Data analysis reveals most of the research related to VBML modality focusing Microsoft Kinect sensor due to its greater capability of capturing minute and depth details of the subject using image and depth sensors Such unique features of Kinect can be useful for reducing freezing of gait (FOG) events in PD and more effective rehabilitation. From the literature survey of obtained articles on PD gait acquisition modalities, it is concluded that the exceptional features of VBML modality gained huge focus (about 50%) towards it as compared to other modalities for more profitable and effective analysis of PD in early stages
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
