9:00 am - 9:10 am - Opening Remarks
9:10 am - 10:00am , Invited Talk 1: Machine learning for solving a clinical task and answering the clinical-related hypothesis
Presenter: Dr. Luca Romeo, University of Macerata, Italy
Abstract: Nowadays, machine learning is applied in almost every field. Machine learning relies entirely on the data; the more the data, the more efficient machine learning is. The ever-growing quantity of data in the neuroscience field opens the realm of possibilities for machine learning to learn a clinical task and answer the clinical-related hypothesis. However, what is the added value that machine learning brings to an ordinary statistical analysis? How machine learning can be exploited to find discriminative information encoded in the data? How machine learning can be exploited to localize where the discriminative information is placed? In this talk, we will try to answer these questions by focusing on the design and the application of machine learning approaches in order to answer specific clinical questions. The theoretical session about machine learning methodologies will be followed by an interactive laboratory where the participants will apply machine learning methodologies in their clinical and experimental benchmark dataset.
10:00 am - 10:20 am: Detection of Mild Cognitive Impairment from Quantitative Analysis of Timed Up and Go (TUG), Mahmoud Seifallah, et al.
10:20 am - 10:40 am: Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection, Reza Basiri et al.
10:40 am - 11:00 am: MAISON - Multimodal AI-based Sensor platform for Older Individuals, Ali Abedi et al.
11:00am - 12 pm, Invited Talk 2: Human Movement and Affective Technology: Opportunities in Self-Directed Physical Rehabilitation
Presenters: Drs. Nadia Berthouze and Temitayo Olugbade, University College London, UK
Abstract: Rehabilitation is one of the most pressing global health needs, with disorders that affect engagement in everyday physical activities being the biggest burden worldwide. Modelling and analysis of human movement presents opportunities to not only gain insight into these illnesses and their impact on daily living, but also create digital technology systems that support physical rehabilitation, from the clinic to the home. What is the state of research and development in this area? Which opportunities are ripe for exploration and where are the barriers to pursuing them? This talk will discuss human movement modelling problems relevant to physical rehabilitation that have been addressed and highlight current limitations with the aim of drawing attention to critical directions. The talk will further present possibilities for physical rehabilitation technology that is integrated in everyday activity for support at affective levels and point beyond simply measuring behaviour to leveraging embodied bottom-up mechanisms that enhance self-perception of the body and its capabilities.
12:00 - 12:15 - Group Discussion and Closing Remarks