Lola CanameroProf. Lola Cañamero

Paris-Seine INEX Chair Neuroscience and Robotics, France
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Robots as tools and models in the study of wellbeing, pleasure and pain
The notions of wellbeing, pleasure and pain are very complex and difficult to assess, measure, or even define in humans and other animals. This is due, to a large extent, not only to their complexity but also to the difficulty in grasping and accessing their subjective elements: as this workshop highlights, researchers are well aware of the limited reliability of self-reports, and of the numerous cases in which not even self-reports can be obtained. For such reasons, models that try to identify and "formalize" key  elements and properties of these phenomena and their interaction could provide valuable tools for their understanding, both theoretical and in clinical contexts. Beyond the more traditional animals models, robot models can offer advantages such as avoiding critical ethical issues, systematic investigation, replicability, behavioral results stemming the hypotheses tested and compared, understanding of the relationships between specific parameters modeled and resulting behavior, or detailed quantitative analysis of results. At the same time, they raise issues concerning the face validity, relevance and significance of the model to understand wellbeing, pleasure and pain in humans, and much care has to be put to ground these models in interdisciplinary research and clinical practice, as well as in clearly delimiting the scope and understanding the limitations of the models. In this talk I will present some examples of robot models of these different notions -- wellbeing, pleasure and pain -- and discuss the above-mentioned issues, as well as some of their potential uses in clinical practice.

Benedikt SchickDr. Benedikt Schick

Clinic of Anaesthesiology and Intensive Care Medicine, University Hospital Ulm, Germany
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Vision of automated pain detection from a medical point of view
Pain cannot be self-reported by analgosedated, mechanically ventilated, critically ill patients who are unable to communicate. The available rating scales to asses pain in these patients are based on more or less specific surrogate parameters. Methods to measure nociceptive have not yet been established in intensive care medicine because their interpretation is subject to many confounding parameters. In this context, a multimodal approach for the assessment of nociception including Machine Learning algorithms seems to be beneficial to close the diagnostic-therapeutic gap in the assessment of pain in critically ill patients.

Claus-Peter DeisslerClaus Deissler

KPUNKT Technologie Marketing GmbH, Stuttgart, Germany
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Automated pain research, from research to economy exploitation

The accurate quantification of pain has long been a challenge in the fields of medicine, psychology, and neuroscience. The advent of technologies enabling precise pain measurement holds significant implications for society, healthcare, and researchers alike. This abstract explores the potential transformative effects of achieving precise pain measurement on these domains.

In healthcare, the ability to measure pain with precision would revolutionize patient care. Clinicians would be equipped with objective data to inform treatment decisions, leading to tailored interventions that address individual pain experiences. Objective pain measurements could aid in the assessment of treatment efficacy and guide personalized pain management strategies, thereby enhancing patient outcomes and reducing the risk of overtreatment or undertreatment. This precision could particularly benefit 2 main groups of patients:

• verbally disabled patients, without the ability to articulate their pain status.
• chronic pain sufferers, as long-term monitoring could uncover patterns, triggers, and responses, leading to improved therapeutic approaches.

Society would witness a paradigm shift in understanding pain as a subjective experience. Precise pain measurement might foster empathy and validation, reducing scepticism or disbelief often faced by individuals with invisible pain conditions.

Speaking about skepticism is also an aspect that needs to be addressed in the care community if it comes to pain measurement. Demonstrating the objective value of it is key to bring solutions to the market:

The development of standardized pain measurement tools could facilitate communication between patients, caregivers, and healthcare providers, fostering a shared language for expressing pain experiences. This could lead to more compassionate and effective care, promoting a culture that values pain management as a crucial component of overall well-being.

From a research perspective, precise pain measurement would open new avenues for investigation. Researchers could delve into the neurobiological underpinnings of pain more deeply, gaining insights into the complex interactions between genetics, environment, and individual pain responses. Longitudinal studies enabled by continuous pain monitoring might uncover risk factors for chronic pain development and provide data for assessing the long-term impact of various interventions. Additionally, large-scale data collection could contribute to the identification of novel pain biomarkers, potentially paving the way for the development of targeted pain therapies. Automated pain localization research could gain a boost by pain measurement devices in combination with other evolving Ai driven technologies.

Ethical considerations surrounding privacy, consent, and data security would become paramount in the era of precise pain measurement. Striking a balance between benefiting patients and safeguarding their personal information would be a critical challenge that stakeholders must address.

In conclusion, achieving precise pain measurement would usher in a new era of healthcare, societal understanding, and research advancement. The ability to objectively quantify pain holds the promise of improving patient care, fostering empathy, and deepening our understanding of pain mechanisms. However, this transformation would also require careful navigation of ethical concerns to ensure that the benefits are maximized while minimizing potential risks.

Albert ali SalahProf. Albert Ali Salah

Department of Information an Computing Science, Utrecht, Netherlands
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Computer vision based assessment of animal pain
Rapid progress in computer based automation approaches creates new opportunities and challenges for animal wellbeing. The newly established AI and Animal Wellbeing Laboratory at Utrecht University seeks to combine veterinary science, animal ethology, and computer science to responsibly address some of these challenges, and to raise awareness on the ethical aspects of using AI in this domain. In this talk, I will focus on image and video based pain estimation for equines and canines. While pattern recognition approaches used in pain estimation for humans are applicable for these problems to some degree, there are specific issues for pain assessment in animals, such as dataset limitations, annotation difficulties, and issues related to the morphology of the animal's body.