Abstract
This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegenerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.
Highlights
In the last decades, the number of patients with neurodegenerative diseases (NDDs) has been growing rapidly, given the remarkable improvements in life expectancy
Single gait task and dual task conditions are compared. Both Random Forest (RF) and Decision Tree (DT), trained on spatio-temporal features obtained by using a Motion Capture System (MCS) and two force plates, exhibit comparable performance achieving accuracy of 76.5% and 75%, respectively
The literature has been reviewed following a sequential thread starting from a panoramic survey of sensor modalities, mainly used for data acquisition, opening a little window on protocols for gait measurements and on the publicly available datasets, going through the description of more significant features up to the final high-level decision support phase, which essentially involves the classification of available data
Summary
The number of patients with neurodegenerative diseases (NDDs) has been growing rapidly, given the remarkable improvements in life expectancy. Neurodegenerative diseases, such as Alzheimer’s Disease (AD), Multiple Sclerosis (MS), Parkinson’s disease (PD), Huntington’s disease (HD), dementia, etc., are not curable. Neurodegenerative diseases share symptoms that involve progressive cognitive decline, limiting everyday functional abilities and leading to motor dysfunctions, including deficits in gait and balance [2], [3]. The continuous and regular monitoring of the mobility performance of elderly people may help diagnosis and assessment of the severity of neurological disorders. Technological and methodological advances have opened up the potential to provide objective measures of mobility performance in order to aid understanding neurological conditions in an automatic fashion [7]
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