Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach

Fleischmann S, Dietz S, Shanbhag J, Wünsch A, Nitschke M, Miehling J, Wartzack S, Leyendecker S, Eskofier B, Koelewijn A (2024)

Publication Type: Journal article, Original article

Publication year: 2024

Journal

ACM Transactions on Intelligent Systems and Technology Association for Computing Machinery (ACM)

Book Volume: 20

Article Number: 20

Journal Issue: 1

DOI: 10.1145/3702646

Authors with CRIS profile

Sophie Fleischmann Sonderforschungsbereich 1483/1 - Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Simon Dietz Machine Learning and Data Analytics Lab Julian Shanbhag Sonderforschungsbereich 1483/1 - Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Marlies Nitschke Machine Learning and Data Analytics Lab Jörg Miehling Chair of Engineering Design (KTmfk) Sandro Wartzack Chair of Engineering Design (KTmfk) Sigrid Leyendecker Institute of Applied Dynamics Björn Eskofier Machine Learning and Data Analytics Lab Anne Koelewijn Juniorprofessur für Computational Movement Science (Stiftungsprofessur)

How to cite

APA:

Fleischmann, S., Dietz, S., Shanbhag, J., Wünsch, A., Nitschke, M., Miehling, J.,... Koelewijn, A. (2024). Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach. ACM Transactions on Intelligent Systems and Technology, 20(1). https://doi.org/10.1145/3702646

MLA:

Fleischmann, Sophie, et al. "Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach." ACM Transactions on Intelligent Systems and Technology 20.1 (2024).

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