TY  - JOUR
AU  - Aldenhoven, Céline Madeleine
AU  - Nissen, Leon
AU  - Heinemann, Marie
AU  - Dogdu, Cem
AU  - Hanke, Alexander
AU  - Jonas, Stephan
AU  - Reimer, Lara Marie
TI  - Real-Time Emotion Recognition Performance of Mobile Devices: A Detailed Analysis of Camera and TrueDepth Sensors Using Apple's ARKit.
JO  - Sensors
VL  - 26
IS  - 3
SN  - 1424-8220
CY  - Basel
PB  - MDPI
M1  - DZNE-2026-00199
SP  - 1060
PY  - 2026
AB  - Facial features hold information about a person's emotions, motor function, or genetic defects. Since most current mobile devices are capable of real-time face detection using cameras and depth sensors, real-time facial analysis can be utilized in several mobile use cases. Understanding the real-time emotion recognition capabilities of device sensors and frameworks is vital for developing new, valid applications. Therefore, we evaluated on-device emotion recognition using Apple's ARKit on an iPhone 14 Pro. A native app elicited 36 blend shape-specific movements and 7 discrete emotions from N=31 healthy adults. Per frame, standardized ARKit blend shapes were classified using a prototype-based cosine similarity metric; performance was summarized as accuracy and area under the receiver operating characteristic curves. Cosine similarity achieved an overall accuracy of 68.3
KW  - Humans
KW  - Emotions: physiology
KW  - Adult
KW  - Male
KW  - Female
KW  - Mobile Applications
KW  - Smartphone
KW  - Young Adult
KW  - Facial Expression
KW  - ARKit (Other)
KW  - emotion recognition (Other)
KW  - face tracking (Other)
KW  - real-time (Other)
KW  - sensors (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41682575
C2  - pmc:PMC12899966
DO  - DOI:10.3390/s26031060
UR  - https://pub.dzne.de/record/285257
ER  -