A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors

Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual's safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may b...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2025-01, Vol.25 (1), p.260
Main Authors: Palazzo, Lucia, Suglia, Vladimiro, Grieco, Sabrina, Buongiorno, Domenico, Brunetti, Antonio, Carnimeo, Leonarda, Amitrano, Federica, Coccia, Armando, Pagano, Gaetano, D'Addio, Giovanni, Bevilacqua, Vitoantonio
Format: Article
Language:English
Subjects:
Activities of daily living
Adult
Algorithms
Analysis
Artificial intelligence
Biometry
Classification
convolutional neural network
Data collection
Deep Learning
Female
Foot drop
Gait
Gait - physiology
gait disorders
Gait Disorders, Neurologic - diagnosis
Gait Disorders, Neurologic - physiopathology
Gait Disorders, Neurologic - rehabilitation
gait recognition
Humans
inertial measurement units
Male
Movement disorders
Neural networks
Neural Networks, Computer
rehabilitation
Sensors
Simulation
Stroke
Support vector machines
Walking
Walking - physiology
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Summary:Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual's safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time.
ISSN:1424-8220
1424-8220
DOI:10.3390/s25010260