Combination of sensor-embedded and secure server-distributed artificial intelligence for healthcare applications

Abstract: The application of artificial intelligence (AI) in the areas of health, care and social participation offers great opportunities but also involves great challenges. Extensive regulatory, ethical and data-security related requirements exist for data recording, storage and processing of respective personalized and patient-related data. “Artificial Intelligence as a Service” (AIaaS) is pushed for consumer applications by global players, which implies data storage on external database server. However, the available solutions do not meet the requirements. Moreover, small and medium-sized enterprises (SMEs) in the field of healthcare fear the loss of data sovereignty and information outflow. In this paper, we propose a secure and resource-efficient approach by embedding AI directly close to the sensor in combination with secure and distributed data processing on local server or certified “Trusted Data Center”. For this purpose, we have developed the Artificial Intelligence for Embedded Systems (AIfES) platform-independent machine learning library in C programming language. It contains a fully configurable deep artificial neural network with feedforward structure. The library can be run directly on a microcontroller and even allows to train the neural network. Possible healthcare applications include direct (pre-) processing of sensor data, sensor calibration, pattern recognition and classification.

Objekt anzeigen

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Combination of sensor-embedded and secure server-distributed artificial intelligence for healthcare applications ; volume:5 ; number:1 ; year:2019 ; pages:29-32 ; extent:4 ; Current directions in biomedical engineering ; 5, Heft 1 (2019), 29-32 (gesamt 4)

Beteiligte, Orts- und Zeitangaben

• Urheber: Gembaczka, Pierre ; Heidemann, Burkhard ; Bennertz, Bernhard ; Groeting, Wolfgang ; Norgall, Thomas ; Seidl, Karsten

Weitere Informationen

• DOI: 10.1515/cdbme-2019-0008
• URN: urn:nbn:de:101:1-2022101214470196993031
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.