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Erschienen in:
A Practical Exercise on Re-engineering Clinical Guideline Models Using Different Representation Languages Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis

verfasst von : Erica Ramirez, Markus Wimmer, Martin Atzmueller

Erschienen in: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

Verlag: Springer International Publishing

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Abstract

Sensor-based methods for human gait analysis often utilize electromyography capturing rich time-series data. Then, for transparent and explainable analysis interpretable methods are of prime importance. This paper presents analytical approaches in a framework for interpretable anomaly detection and classification of multivariate time series for human gait analysis. We exemplify the application utilizing a real-world medical dataset in the biomechanical orthopedics domain.

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Vorheriges Kapitel A Computational Framework Towards Medical Image Explanation
Nächstes Kapitel Self-organizing Maps Using Acoustic Features for Prediction of State Change in Bipolar Disorder
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Metadaten
Titel
A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis
verfasst von
Erica Ramirez
Markus Wimmer
Martin Atzmueller
Copyright-Jahr
2019
Buch
Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

Print ISBN: 978-3-030-37445-7

Electronic ISBN: 978-3-030-37446-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-37446-4_11