Otto-von-Guericke-Universität Magdeburg

 
 
 
 
 
 
 
 

Automatic Pain Recognition based on Facial Expression and Psychobiological Parameters

The assessment of acute pain is one of the basic tasks in clinics. To this day, the common practice is to rely on the utterance of the patient. For mentally affected patients this is little reliable and valid. For non-vigilant people or newborns it cannot be used at all. However, there are several characteristics that indicate pain. These include specific changes in the facial expression and in psychobiological parameters like heart rate, skin conductance or electrical activity of skeletal muscles.

We are working towards an automatic system, which can distinguish whether a patient feels pain or not, and can assess the intensity of the pain. Based on experiences in facial expression recognition our system can distinguish facial expressions of pain from others and rate the intensity of the expression. In the current comprehensive study, we investigate the relations between pain, the facial expressions and the psychobiological feedback. The results are used to improve the robustness, reliability and validity of our system. Further, the data recorded in the study, which is named BioVid Heat Pain Database, is available to the scientific community. In the project, we collaborate with the Emotion Lab of the University of Ulm.

 

pain_intensity.jpg
Pain expression intensity for a video sequence with sample frames.

 

Contact: Philipp Werner, Ayoub Al-Hamadi

Letzte Änderung: 08.11.2017 - Ansprechpartner: Dipl.-Ing. Arno Krüger
 
 
 
 
image_pose
Video: Head pose and orientation
 
 
 
 
v01_winglets
Video: Particle Tracking
 
 
 
 
Johanniskirche_1_300x240
Video: Multi-object tracking
 
 
 
 
Test_584
Video: Gestures and intention
 
 
 
 
Kinect-pose-ayoub1-logo
Video: Pose and Face detection using Kinect
 
 
 
 
kinect_pose
Video: HCI Face Attention
 
 
 
 
mimik-flow1
Video: Static and dynamic features
 
 
 
 
Motionblobs-gut
Video: Trisectional Multi-object tracking
 
 
 
 
s8
Video: Ephestia Parasitization