{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:50:07Z","timestamp":1778255407869,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KIT-Publication Fund of the Karlsruhe Institute of Technology","award":["3293"],"award-info":[{"award-number":["3293"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient\u2019s tissue require precise sensor data. However, since the deformation of the patient\u2019s body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.<\/jats:p>","DOI":"10.3390\/s24092895","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T08:59:39Z","timestamp":1714553979000},"page":"2895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0646-8061","authenticated-orcid":false,"given":"Andreas","family":"Puder","sequence":"first","affiliation":[{"name":"Embedded Systems, Getinge AB, 76437 Rastatt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2386-7021","authenticated-orcid":false,"given":"Moritz","family":"Zink","sequence":"additional","affiliation":[{"name":"Institute for Information Processing Technologies (ITIV), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7263-3240","authenticated-orcid":false,"given":"Luca","family":"Seidel","sequence":"additional","affiliation":[{"name":"Institute for Information Processing Technologies (ITIV), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Sax","sequence":"additional","affiliation":[{"name":"Institute for Information Processing Technologies (ITIV), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/S0140-6736(09)61460-4","article-title":"Ageing populations: The challenges ahead","volume":"374","author":"Christensen","year":"2009","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/S0140-6736(14)61347-7","article-title":"The burden of disease in older people and implications for health policy and practice","volume":"385","author":"Prince","year":"2015","journal-title":"Lancet"},{"key":"ref_3","first-page":"28","article-title":"Research progress on population aging and chronic diseases","volume":"3","author":"Qiao","year":"2023","journal-title":"Meds Public Health Prev. 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