The manuscript “Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach” by Andrea Ferrario (Mobiliar Lab for Analytics at ETH and Chair of Technology Marketing, ETH) et al., has been accepted for publication in JMIR Aging. The authors show that machine learning may support the prediction of working memory in healthy older adults, using linguistic measures and social context information extracted unobtrusively from their everyday conversations.
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- Delegating to Agentic AI
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- Human Robot Interaction
- Transformers for Emotion Recognition
- Neuromarkers for Cognitive Load
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- AR for Collaboration
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