September 3rd, 2024 
Daniela Lopez de Luise
1st Workshop on Advancing Non-invasive Human Motion Characterization in the Clinical Domain: Methods and Applications (ANIMA)
Workshop at BMVC, Glasgow, UK. 28th November
https://anima2024.sites.uu.nl/
Deadline for submission extended to September 5!
Motivation and topics
In the healthcare domain, understanding and characterizing human motion is essential for tasks, including diagnostics, monitoring and rehabilitation. Traditionally, the gold standard to accurately characterize and study human motion relies on motion capture systems and physical markers placed on the skin. These techniques are intrusive, expensive and they may limit natural movements. Furthermore, they limit the natural environment in which the analysis can take place. Recently, video analysis has become an increasingly viable alternative to marker-based systems to perform human motion analysis. This is due to the increasing progress – in terms of accuracy and computational resources needed – of deep learning algorithms in solving computer vision problems. In particular, recent advancements in deep learning-based Human Pose Estimation (HPE) algorithms enable the automated quantitative analysis of human motion from video data.
The application of computer vision in healthcare has the potential to revolutionize how we analyze human behavior. This workshop is positioned at the intersection of computer vision and medical applications, emphasizing the importance of extracting meaningful insights from video data. Our primary interest lies in the behavioral analysis of human motion. This focus is particularly crucial in healthcare, where precise understanding of an individual's movements can aid in early detection of neuromotor disorders, personalized care plans and effective rehabilitation strategies.
The medical domain poses unique challenges in ensuring robustness and high accuracy. Moreover, clinical applications require tailoring to specific demographics such as infants, elderly, or people with physical impairments. Consequently, dealing with data scarcity for training and benchmarking is another challenge. Our workshop aims to contribute to the broader computer vision community by focusing on those challenges that are inherent, but not unique, to the medical domain. We believe that tackling these topics in behavioral motion analysis within the medical domain will not only advance healthcare technology but also push the boundaries of computer vision research.
Topics of the workshop
- Motion quantification: measurement of human pose and motion in 2D or 3D, including multimodal approaches.
- Motion classification: detection of specific human motions, training classifiers with limited data.
- Clinical datasets: dealing with data scarcity, privacy, federated learning, synthetic data, and benchmarking.
- Motion recording: use, calibration and combination of various sensors.
- Applications: in the domain of infant analysis, diagnostics and rehabilitation.
- Real-time analysis: algorithms to perform human motion analysis in real-time, enabling applications such as continuous monitoring in clinical settings.
- Ethical considerations: studies that address ethical implications of using computer vision in healthcare, including issues related to privacy, consent and bias in algorithmic decision-making.
Invited speakers
Dr. Dimitris Tzionas is an assistant professor at the University of Amsterdam. He conducts research on the intersection of Computer Vision, Computer Graphics and Machine Learning. His motivation is to understand and model how people look, move and interact with the physical world and with each other to perform tasks. This involves: (1) accurately “capturing” real people and their whole-body interactions with scenes and objects, (2) modeling their shape, pose and interaction relationships, (3) applying these models to reconstruct real-life actions in 3D/4D and (4) using these models to generate realistic interacting avatars in 3D/4D. Potential applications include Ambient Intelligence, Virtual Assistants, Human-Computer/Robot Interaction and Mixed Reality. The long-term goal is to develop human-centered AI that perceives humans, understands their behavior and helps them to achieve their goals.
Dr. Logan Wade is a Research Fellow at the University of Bath, United Kingdom. As a clinical biomechanist, his research harnesses computer vision and machine learning to identify how patients move, with the goal of integrating biomechanical measures into clinical practice. Recent advances in Artificial Intelligence has seen the rise of motion capture methods that are fast and minimally invasive, allowing collection of data in clinics that was previously restricted to high-end biomechanical laboratories. However, while the accuracy of these systems has drastically improved over the past decade, determining if their accuracy is sufficient for use on an individual patient level is still to be determined. His long-term goal is to develop computer vision tools that are clinically relevant, employing mediums such as markerless video capture to identify movements of the body and 3D ultrasound to examine patient-specific spinal postures.
Dr. Sara Moccia is an Associate Professor in bioegineering at Universit`a degli Studi G. d’Annunzio (Chieti, Italy). She works on designing AI algorithms for clinical data analysis, with a specific focus on preterm infants’ care. She is the author of more than 50 papers. She is PI for three research projects for a total budget of around 2 mln euro. She serves as Associate editor for two international journal and currently as program chair for IPCAI
Dr. Simona Tiribelli is the director for AI Ethics of the Institute for Technology & Global Health at the MIT-funded spin-off PathCheck Foundation (Boston, US), assistant professor at the University of Macerata (Italy), where she teaches Ethics of Artificial Intelligence and Global Justice and Technology, 2023 visiting scholar in AI ethics at the New York University (NYU), and 2020 Fulbright awarded and fellow at the MIT Media Lab, Massachusetts Institute of Technology, US. She is also a founder of the spin-off GAIA (AI Ethics and Governance) and AI Ethics advisor for companies in Europe and US. She authored two books and a number of articles in leading scientific international journals on ethics of artificial intelligence and digital technology, and delivered on invite more than 50 talks in academic institutions such as Harvard University, Tufts University, Toronto University, and many more, in Europe, Canada, and USA.
Important dates
Paper submission: September 5th, 2024
Notification of acceptance: September 18th, 2024
Camera-ready submission: September 30th, 2024
Submission
Workshop papers should adhere to the paper guidelines of the main conference: https://bmvc2024.org/authors/author-guidelines/ Accepted papers will be included in the BMVC workshop proceedings published and DOI-indexed by BMVA. Submissions can be made through the submission system: https://cmt3.research.microsoft.com/ANIMA2024/
Organizers
Lucia Migliorelli: Department of Information Engineering, Marche Polytechnic University, Italy, l.migliorelli@staff.univpm.it
Matteo Moro: Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova & Machine Learning Genoa (MaLGa) Center, Genova, Italy, matteo.moro@unige.it
Ronald Poppe: Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands, r.w.poppe@uu.nl
ICCPR2024CFP.pdf