English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Gait analysis, an expanding research area, employs non invasive sensors andmachine learning techniques for a range of applicatio ns. In this study, weconcentrate on gait analysis for detecting cognitive decline in Parkinson'sdisease (PD) and under dual task conditions. Using convolutional neuralnetworks (CNNs) and explainable machine learning, we objectively analyze gaitdata and associate findings with clinically relevant biomarkers. This isaccomplished by connecting machine learning outputs to decisions based on humanvisual observations or derived quantitative gait parameters, which are testedand routinely implemented in curr ent healthcare practice. Our analysis of gaitdeterioration due to cognitive decline in PD enables robust results using theproposed methods for assessing PD severity from ground reaction force (GRF)data. We achieved classification accuracies of 98% F1 sc ores for eachPhysioNet.org dataset and 95.5% F1 scores for the combined PhysioNet dataset.By linking clinically observable features to the model outputs, we demonstratethe impact of PD severity on gait. Furthermore, we explore the significance ofcognit ive load in healthy gait analysis, resulting in robust classificationaccuracies of 100% F1 scores for subject identity verification. We alsoidentify weaker features crucial for model predictions using Layer WiseRelevance Propagation. A notable finding o f this study reveals that cognitivedeterioration's effect on gait influences body balance and foot landing/liftingdynamics in both classification cases: cognitive load in healthy gait andcognitive decline in PD gait.

Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis