Bart Jansen is a professor at the department of Electronics and Informatics (ETRO) at the Vrije Universiteit Brussel. He is interested in developing image and signal processing methods and artificial intelligence methods for a variety of applications in the broad biomedical engineering domain, but mainly focusing on rehabilitation engineering. He builds software and algorithms to understand and improve the rehabilitation process, including computer games, algorithms for dynamic difficulty adjustment, biomechanic analysis from low cost sensors, a variety of AI methods on medical data, augmented reality in medical applications and many more.

Bart Jansen graduated in 2001 as a Master of  Science  in  Computer  Science, obtained a master after master in AI in 2003 and obtained a PhD in computer science (VUB Ai-lab, promotor Luc Steels) in 2005, all from the Vrije Universiteit Brussel. Since 2006, he is working at the department of electronics and informatics (ETRO) at VUB. In 2016 he became a professor and since 2020 he also has a 10% appointment with imec.

Bart Jansen is teaching computer science and AI related courses in the bachelor of Engineering Sciences as well as the master in Applied Computer Science, the master in Biomedical Engineering and the Postgraduate on Rehabilitation and Human Sustainable Technology.

"My ultimate goal is to advance the biomedical engineering domain to respond to the needs of people with disabilities."

Research Interests 

Rehabilitation Engineering

Rehabilitation engineering is a term with many different definitions. Some consider it to be a rather broad domain focussing on assessing and responding to the needs of people with disabilities, others (like the EMBS) define it as creating methods and technologies to help patients regain cognitive and/or motor function.

I am driven by the observation that in physical and cognitive rehabilitation there are several important aspects that could improve the rehabilitation process for the patients in a crucial manner.

One of those is the poor quantification of treatment effect: a fine grained monitoring of patients (e.g. suffering from neurodegenerative or other conditions) does not exist. Hence, evolution of for instance motor skills are poorly quantified and the effects of treatment options on these are largely unquantified at an individual basis. This is not surprising, as such an analysis requires specialised tools and examinations as for instance functional assessment at a gait lab. Such assessment is not available nor feasible or desirable on a very frequent basis for a large group of people. Rather, more frequent or even continuous assessment, easily accessible or even ubiquitous, could provide a through quantification of the cognitive and motor functions of the subject. This would allow for a detailed quantification (and later on understanding) of the impact of various rehabilitation schemes. In this domain, we mainly focus on the development of tools and methods for physical assessment by means of low-cost mass market sensor devices, including the Wii balance board, the Microsoft Kinect and others. Besides improving the quality of care and the efficiency of the rehabilitation process, our tools also provide digital biomarkers for pharmaceutical studies focusing on various interventions on subjects with disabilities of movement disorders.

A second issue with current rehabilitation practices is the suboptimal compliance and adherence to optimal rehabilitation exercise doses. Rehabilitation is a slow process requiring a lot of repetitions and practising. Weekly, or even daily, one-on-one sessions with a rehabilitation specialist are hence often not sufficient, driving the prescription of for instance home-based exercises. However, a vast minority of subjects comply to these, as they are perceived as being difficult, painful, boring or even useless. Game-based rehabilitation concerns the use of computer games to present, assess and steer the rehabilitation exercises and might actually improve adherence and compliance. However, whereas real-life therapists excell in adapting therapeutic interventions to the needs of every individual subjects, computer games are fit-for-all and rigid. We investigate the development and use of a flexible and adaptive game based rehabilitation platform.

Neuro Evolution

Neuro Evolution concerns the use of genetic algorithms to evolve neural networks. As this is an optimization approach which is not using gradient information, it is a slow and computationally intensive search process. However, the powerful search strategies provided by contemporary genetic algorithms allows for the joint optimization of network topology and network weights (e.g. NEAT). Our research focuses on integrating feature selection as a third component in this joint optimization process and has resulted in the development of FD-NEAT, a successor of NEAT performing feature selection, network topology optimization and weight optimization.

Augmented Reality

Although seriously hyped, the adoption of augmented reality beyond gadgets is rather poor. Both improvements in terms of hardware (glasses) as well as applications are needed. In this domain, we focus on developing niche applications for instance in medical and look into visualisation of medical image data and object recognition.

  • Affiliated to imec.
  • Member of the etro iof and the iof.
  • Member of steering board of the Brubotics Rehabilitation Research Center.
  • Member of IEEE.
  • Member of ACM and SIGEVO (Special Interest Group on Genetic and Evolutionary Computation) special interest group.
  • Member of ESMAC, the European Society for Movement Analysis in Adults and Children.
  • Member of ISVR (International Society for Virtual Rehabilitation).
  • Member of FARI, the AI for the Common Good Institute Brussels.
  • Maxine Tan – Development of a Feature-Selective Neuroevolution Method and its relevance in Medical CAD Applications – 2011.
  • Frederik Temmermans – Visual search in mobile and medical applications: Feature extraction and classification, interoperable image search and human-machine interaction – 2014.
  • Xiaolan Yao – Mobility Monitoring in Elderly Persons: towards a refinement of clinical assessment scales and home surveillance – Januari 2015.
  • Lubos Omelina – Visual human recognition and identification – November 2016 – Joint Phd with Slovak University of Technology.
  • Bruno Bonnechere – Functional assessments during physical rehabilitation exercises using serious games – Januari 2019 – Joint Phd with ULB.
  • Evgenia Papavasileou – Feature selection and classification in high dimensional data based on neuro-evolution – March 2021.
  • Alexander Sonora – Robust feature extraction for automated characterisation of medical images. Februari 2021 · Joint Phd met Universidad de Oriente, Cuba – copromotor.
  • Veerle Knoop – The role of muscle fatigability and self-perceived fatigue in the development of frailty in the oldest old – August 2021 – copromotor.
  • Panagiotis Tsinganos – Multi-channel emg pattern classification based on deep learning – November 2021 · Joint Phd with university of Patras.
  • Emma De Keersmaecker – Virtual reality for gait rehabilitation: effect and effectiveness of virtual reality and optic flow in a neurological population – January 2023.
  • Redona Brahimetaj – Classification of breast cancer – in vitro microcalcification analysis in micro-ct images – May 2023.
  • Geletaw Sahle – Developing Clinical decision support instruments for the point-of-care in low resource settings – September 2023.