@inproceedings{elfert2021,Author = {Elfert, Patrick and Tiryaki, Enes and Eichelberg, Marco and Rösch, Norbert and Hein, Andreas},Title = {A Deep Learning Assisted Digital Nutrition Diary to Support Nutrition Counseling for People Affected by the Geriatric Frailty Syndrome},Year = {2021},Pages = {(in print)},Booktitle = {Proceedings 2021 IEEE Symposium on Computers and Communications (ISCC)},type = {inproceedings},Abstract = {Although digitization and artificial intelligence are already being used in many areas, nutritional counseling for frailty patients is largely retrospective and based on analog questionnaires. In order to enable a broad spectrum of frailty patients to independently keep a digital nutrition diary, this paper presents a hybrid input method based on object detection using artificial intelligence in combination with a dynamic and interactive interview mode. The interview mode dynamically queries for possible missing inputs based on the objects detected by the artificial intelligence. The artificial intelligence was trained with open source and specifically for this use case generated data. A study with 21 subjects compared the hybrid approach with four other approaches and mobile applications based on usability, time effort, and detection accuracy. Especially in the area of usability, the hybrid approach came out on top.}}@COMMENT{Bibtex file generated on }