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Assistive technologies have the goal to provide greater quality of life and independence in domestic environments by enhancing or changing the way people perform activities of daily living (ADLs), tailoring specific functionalities to the needs of the users. Significant advances have been made in intelligent adaptive technologies that adopt state-of-the-art learning systems applied to assistive and health-care-related domains. Prominent examples are fall detection systems that can detect domestic fall events through the use of wearable physiological sensors or non-invasive vision-based approaches, and body gait assessment for physical rehabilitation and the detection of abnormal body motion patterns, e.g., linked to age-related cognitive declines. In addition to an adequate sensor technology, such approaches require methods able to process rich streams of (often noisy) information with real-time performance.





Assistive technology has been the focus of research in the past decades. However, it flourished in the past years with the fast development of personal robots, smart homes, and embedded systems. The focus of this workshop is to gather neural network researchers, both with application and development focus, working on or being interested in building and deploying such systems. Despite the high impact and application potential of assistive systems for the society, there is still a significant gap between what is developed by researchers and the applicability of such solutions in real-world scenarios. This workshop will discuss how to alleviate this gap with help of the latest neural network research such as deep, self-organizing, generative and recurrent neural models for adaptable lifelong learning applications. In this workshop, we aim at collecting novel methods, computational models, and experimental strategies for intelligent assistive systems such as body motion and behavior assessment, rehabilitation and assisted living technologies, multisensory frameworks, navigation assistance, affective computing, and more accessible human-computer interaction.

The primary list of topics covers the following topics (but not limited to):
  • Machine learning and neural networks for assistive computing
  • Behavioral studies on assistive computing
  • Models of behavior processing and learning
  • New theories and findings on assistive computing
  • human-machine, human-agent and human-robot interaction focused on assistive computing
  • Brain-machine interfaces for assistive computing
  • Crossmodal models for assistive computing

Important dates:

Paper submission deadline: April 06, 2018
Notification of acceptance: May 04, 2018
Camera-ready version: May 25, 2018
Workshop: July 8, 2018


Selected contributions will be presented during the workshop as spotlight talks and in a poster session.

Contributors to the workshop will be invited to submit extended versions of the manuscripts to a special issue to be arranged. Submissions will be peer reviewed consistent with the journal practices.