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A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis
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References 34 · Cited by 17
Research works printed but not linked: 2
- Atzmueller, M.: Onto explicative data mining: exploratory, interpretable and explainable analysis. In: Proceedings of the DBDBD 2017. TU Eindhoven, Netherlands (2017)
- Keleş, S., Subaşı, A.: Classification of EMG signals using decision tree methods. In: Proceedings of the International Symposium on Sustainable Development (ISSD) (2012)
Metadata
- Authors
- Ramirez, E., Wimmer, M., & Atzmueller, M.
- Affiliations
- Tilburg University (NL)
- Rush University Medical Center (US)
- Corresponding author
- Atzmueller, Martin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 132-147
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2998413740
- Cited by
- 17
- References
- 34
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 6 · 2020: 2 · 2019: 1
- FWCI
- 0.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- interpretable anomaly detection, multivariate time series classification, gait analysis, electromyography, explainable machine learning, orthopedics, biomechanics
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis. https://app.cochat.ai/papers/10.1007/978-3-030-37446-4_11
- The paper
- Ramirez, E., Wimmer, M., & Atzmueller, M. (2019). A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-37446-4_11
