There are many potential applications of AI in healthcare, particularly in the care of older people. This mainly refers to algorithm-based computing techniques that manage and analyze large data sets to make conclusions and predictions: from clinical decision support systems that can help detect delirium from medical records to wearable devices that can predict the risk of falls.
In this webinar, the needs and challenges of older people will be presented and defined and possible solutions based on artificial intelligence methods for specific application examples will be demonstrated.
Program
12h00-12h10: Presentation of Innovation Booster SILVER AGING 12h10-12h20: Presentation of Innovation Booster ARTIFICIAL INTELLIGENCE 12h20-12h30 : Dr. Vincent Grek – AI, medicine and sport for elderly people 12h30-12h40 : Daria Mühlethaler – Supporting the Care Givers with GenAI 12h40-12h50: Q&A 12h50-13h00 : Prof. Dr. Tobias Nef – AI and Digital Biomarkers for patients with Neurodegenerative diseases 13h00-13h10 : Dennis Eitner 13h10-13h20 : Q&A 13h20-13h30 : Wrap-up
Due to rising social expectations and new laws on the responsible use of artificial intelligence (AI), it is becoming increasingly important for companies to ensure the trustworthiness and ethical acceptability of AI applications. However, instead of viewing these requirements merely as a challenge, they also offer companies the opportunity to innovate and differentiate themselves from the competition. The aim of this event is to jointly explore the business challenges in the area of responsible AI. It will also discuss how companies can proactively use these requirements to tap into new innovation opportunities and position themselves sustainably in the market.
The Service à l’Innovation et à la Recherche (SIR) of the HES-SO is organizing a webinar on June 24, 2024 (12:00-13:00) to present two ideas supported by the Innosuisse Innovation Booster program. The funding opportunities in the frame of the Innovation Booster databooster and Artificial Intelligence, for which Nabil Abdennadher, professor at HEPIA, is the representative in French-speaking Switzerland, will be introduced and explained.
The Workshop on Challenges in Data Management in Robotics aims to bring together robotics enthusiasts, researchers, and industry professionals to discuss and address the pressing issues surrounding data management in robotics applications. Through keynotes, case studies, and breakout groups, participants will gain valuable insights into tackling data-related challenges in robotics and explore potential solutions.
Understanding human practices within the context of human-robot interaction is crucial for robots to adjust their behaviors appropriately. However, this presents several challenges. Not only do technical tasks and ethical considerations come into play, but also the fact that different individuals interact in unique ways, making the development of universally applicable algorithms extremely difficult. Furthermore, ensuring precise communication and task allocation in the human-robot workflow is essential, especially considering the proximity between humans and robots.
Most robots work with both filtered and uncensored data, not only for robot training and learning tasks but also directly acquired in-process by physical sensors. Nowadays, tasks such as training may require a large amount of data. This can be challenging in terms of data overload, costs of data analytics integration, and robust data management practices.
Both humans and machines occasionally fail at specific tasks, making it essential to consider failure scenarios in developing robust interaction frameworks. By understanding and leveraging failure data, we aim to improve the design, development, control, and robustness robots, ensuring more resilient and adaptive human-robot interactions. To achieve this, we will explore methods for identifying and capturing failure events, analyzing their causes, and utilizing this data to improve the reliability and performance of robotic systems.
One aspect of robustness involves the capacity for a robot to enhance its knowledge and behavior autonomously. This means it should possess a degree of flexibility to adapt and apply abilities to new situations as needed. Achieving this context-driven, adaptive autonomy, which relies on common-sense knowledge and practical manipulation tasks, demands extensive programming and often involves on-platform data management & analytics.
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