TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Bach, Thanh Nam
A1  - Junger, Denise
A1  - Curio, Cristóbal
A1  - Burgert, Oliver
T1  - Towards human action recognition during surgeries using de-identified video data
JF  - Current directions in biomedical engineering
N2  - With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.
KW  - action recognition
KW  - de-identification
KW  - sensitive information
KW  - image data
KW  - YOLO
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-37513
SN  - 2364-5504
SS  - 2364-5504
U6  - https://doi.org/10.1515/cdbme-2022-0028
DO  - https://doi.org/10.1515/cdbme-2022-0028
VL  - 8
IS  - 1
SP  - 109
EP  - 112
S1  - 4
PB  - De Gruyter
CY  - Berlin
ER  -