Sensors

| 2021

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

Chariklia Chatzaki, Vasileios Skaramagkas, Nikolaos Tachos, Georgios Christodoulakis, Evangelia Maniadi, Zinovia Kefalopoulou, Dimitrios I. Fotiadis, Manolis Tsiknakis

Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University

Keywords

gait analysis, parkinson’s disease, insoles, pressure sensors, dataset

Abstract

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.

Moticon's Summary

Th In this study the authors introduced a smart-insole dataset with the aim to aid in developing and evaluating computational methods for gait analysis. The data set was collected using a walk straight and turn test as well as a modified version of the timed up and go test. Testing was performed with three different subject groups including healthy adults, elderly and Parkinson's disease patients. Data collection was performed using Moticon sensor insoles. The dataset revealed distinct gait characteristics for the three different subject groups and verified assumptions on gait characteristics specifically regarding the groups of elderly and Parkinson's disease patients.

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