{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:17:54Z","timestamp":1780078674574,"version":"3.54.0"},"reference-count":26,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["01MT20001A"],"award-info":[{"award-number":["01MT20001A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human\u2013robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human\u2013robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human\u2013robot interaction.<\/jats:p>","DOI":"10.3390\/s21217210","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human\u2013Robot Interaction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0488-7676","authenticated-orcid":false,"given":"Christoph","family":"Scholl","sequence":"first","affiliation":[{"name":"Siemens AG, Technology, Guenther-Scharowsky-Str. 1, 91058 Erlangen, Germany"},{"name":"Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Carl-Thiersch-Stra\u00dfe 2b, 91052 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2154-4950","authenticated-orcid":false,"given":"Andreas","family":"Tobola","sequence":"additional","affiliation":[{"name":"Siemens AG, Technology, Guenther-Scharowsky-Str. 1, 91058 Erlangen, Germany"},{"name":"Institute of Electronics Engineering, Faculty of Engineering, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Cauerstr. 9, 91054 Erlangen, Germany"},{"name":"Faculty of Electrical Engineering, Precision Engineering, Information Technology, Nuremberg Institute of Technology, Wassertorstra\u00dfe 10, 90489 N\u00fcrnberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Ludwig","sequence":"additional","affiliation":[{"name":"Siemens AG, Technology, Guenther-Scharowsky-Str. 1, 91058 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5886-0597","authenticated-orcid":false,"given":"Dario","family":"Zanca","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Carl-Thiersch-Stra\u00dfe 2b, 91052 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-0336","authenticated-orcid":false,"given":"Bjoern M.","family":"Eskofier","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Carl-Thiersch-Stra\u00dfe 2b, 91052 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Arents, J., Abolins, V., Judvaitis, J., Vismanis, O., Oraby, A., and Ozols, K. 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