Successful Paper: Machine Learning & Gait Analysis
Top Ranking and Presentation at International Journal Club
The paper “Explaining Machine Learning Models for Clinical Gait Analysis” published in December 2021 addresses a key problem that arises when it comes to actually using Machine Learning (ML) as a support tool in medical decision-making in gait analysis: due to their complexity, it is virtually impossible to comprehend how such algorithms arrive at decisions, which is why they are often referred to as “black box”.
The team headed by Djordje Slijepčević and Brian Horsak is among the first research groups to demonstrate how these black box decisions can be rendered comprehensible and transparent for clinicians. This can help to improve the confidence of medical staff in this method and its decisions and to extract new insights from very large data volumes at the same time.
Top Downloads and Inclusion in Journal Club!
The paper was published in the up-and-coming journal “ACM Transactions on Computing for Healthcare” where it is the second most downloaded paper after only two months!
What is more, Djordje Slijepčević submitted the paper to the first International Journal Club of the “European Society of Biomechanics" (ESB). Among the submissions, a total of four already published papers were selected by the EBS – among them the work of our researchers.
Djordje Slijepčević will present the paper online within the framework of the Journal Club on 8 March 2022 (4 pm) – the event is open to everyone interested.
View Teaser-Video:
Further information:
Dipl.-Ing. Djordje Slijepčević, BSc
ResearcherMedia Computing Research Group
Institute of Creative\Media/Technologies Department of Media and Digital Technologies