Pain is highly individual and subjective and the translation of nociception into pain perception can be curtailed by stress or exacerbated by anticipation. Today, objective measurements of pain are lacking and with this a good basis for pain treatment. Two types of pain can be distinguished, acute pain provoked by a specific disease or injury and chronic pain which outlasts the normal time of healing.
First observational studies have been conducted in this area on usage of autonomic nervous system measurements (GSR, ECG,..) for quantification of pain.
For the PhD project we propose a combination of sensing methods and machine learning for acute and chronic pain models. Sensing methods can include autonomic nervous system measurements, as Imec did in the context of stress measurement, facial expression (to be developed) and objective motion, movement measurements. This will be combined by subjective measurements and context measurements.
The desired outcome is one / more pain measurement models combining objective and subjective data.
Required background: bio-medical engineering, data analytics, machine learning
Type of work: 40% data collection, 60% data analytics & modeling
Supervisor: Chris Van Hoof
Daily advisor: Walter De Raedt
The reference code for this position is 1812-65. Mention this reference code on your application form.