Among existing challenges both in clinical and non-clinical context, it is the ability to objectively measure and determine pain. Pain is the first reason for a doctor consultation and is a major concern for the analgesic dosage in pre/post-operatory phases, and among population unable to express own feeling, such as children, comatose, lock-in syndrome. Pain sensation and perception have multi-facet nature (acute vs chronic, nociceptive vs neuropathic, physical vs emotional). Pain is also highly individual and can be influenced several factors (gender, ethnicity, environmental, economic, lifestyle). The complex nature of pain assessment represents a suitable candidate problem for AI.
The objective of this PhD is to investigate the use of AI and digital health data (physiological and lifestyle data) as obtained via wearable devices, video system, e-diary, mobile technology and medical record (from public and/or internal databases) for modeling acute and chronic pain. The desired outcome is one / more pain measurement models combining objective and subjective data. The PhD candidate will also investigate methods to suppress the feeling of pain through various stimulation mechanics.
The candidate will leverage imec competences in wearable-based physiological sensing (i.e. Galvanic Skin Response, Electrocardiography, Photoplethysmography) and datascience expertise (i.e. modeling of autonomous nervous system response to stress) to perform his/her research. The candidate will work in a multidisciplinary team of biomedical engineers, data scientists, research scientists, and will be able to consult medical experts for the definition, development and the validation of one / more use case scenarios.
- AI & machine-learning
- digital signal processing and mathematics
- affinity with electronics devices and experimental measurements
- basic human physiology (or willingness to learn)
Required background: Electrical engineering, computer science, data science, biomedical engineering
Type of work: 30% data collection trials, 60% signal processing & algorithm development
Supervisor: Chris Van Hoof
Daily advisor: Nick Van Helleputte
The reference code for this position is 2020-104. Mention this reference code on your application form.
Chinese nationals who wish to apply for the CSC scholarship, should use the following code when applying for this topic: CSC2020-53.