Abstract
Abstract Better handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.
Highlights
Parkinson’s disease (PD) is considered to be the second most common age-related neurodegenerative disorder after Alzheimer’s disease
A relationship between clinical features and freezing of gait with respect to the gait abnormalities was outlined in [25]. 30 patients with PD divided into two subgroups: (i) tremordominant (TD) group and postural instability and gait disturbance (PIGD) group (ii) freezing of gait and nonfreezing of gait group were taken for these studies using a computerised video motion analysis system
We first introduced the importance of PD identification followed by its relation to the abnormalities in gait of the subjects with PD
Summary
Parkinson’s disease (PD) is considered to be the second most common age-related neurodegenerative disorder after Alzheimer’s disease. It is well known that abnormalities of gait are seen in people with PD [2] This is due to the result from varying combinations of hypokinesia, rigidity as well as from the defects of posture and equilibrium that includes the characteristics of shuffling gait with small steps and poverty of movements in the trunk. Due to this fact, it was identified that the early identification of this abnormal gait leads to the identification of PD in advance. In order to limit the review, only technqiues which use deep learning for pose estimation, gait analysis and their combination for early identification of PD were discussed
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