The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson's Patients

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-04, Vol.21 (8), p.2821
Main Authors: Chatzaki, Chariklia, Skaramagkas, Vasileios, Tachos, Nikolaos, Christodoulakis, Georgios, Maniadi, Evangelia, Kefalopoulou, Zinovia, Fotiadis, Dimitrios I, Tsiknakis, Manolis
Format: Article
Language:English
Subjects:
Adult
Aged
Annotations
dataset
Datasets
Gait
Gait Analysis
Humans
Insoles
Laboratories
Older people
Parkinson Disease - diagnosis
Parkinson's disease
Postural Balance
Pressure sensors
Sensors
Systems analysis
Time and Motion Studies
Values
Walking
Wearable Electronic Devices
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Summary:Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson's disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson's Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson's disease.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21082821