Human Activity Recognition Using Wearable Sensors for Learning and Teaching
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11970
Special Issue Editors
Interests: multiple representations; multimedia learning; technology-enhanced learning; AI and education
Interests: multiple representations; representational competence; eye-tracking in education; AI and education
Interests: pervasive and wearable computing; sensor systems; artificial intelligence; embedded systems; self-organization
Special Issue Information
Dear Colleagues,
Human activity recognition (HAR) is a well-established, dynamic research area within ubiquitous and wearable computing. As sensors are becoming increasingly ubiquitous and the machine learning methods needed to evaluate the collected information are improving, we are seeing a rapid emergence of practical, mature applications in fields such as, for example, Industry 4.0, sports, and healthcare. While the use of sensors has also been extensively studied in education, the use of complex activity recognition (rather than the mere sensing and simple evaluation of sensor signals) is still at a very early stage. One reason for this may be that not only do the complexity and variety of bodily activities make it difficult to automatically recognize such activities rapidly and accurately but, also, that learning variables such as cognition or emotion are invisible and need to be interpreted by human‐driven interpretations or validated test instruments. In combination with these interpretation methods, state-of-the-art deep learning models could overcome several problems in HAR, for instance, feature extraction and multiple-occupant activities and, thus, advance the interpretation of sensor-based HAR data.
This Special Issue aims to address this trade-off and discuss the state of the art, difficulties, innovations, and improvements in HAR applications in the context of cognition, emotions, learning, and instruction.
Prof. Dr. Jochen Kuhn
Dr. Stefan Küchemann
Prof. Dr. Paul Lukowicz
Prof. Dr. Albrecht Schmidt
Guest Editors
Manuscript Submission Information
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Keywords
- human activity recognition
- wearable sensors
- cognition
- emotion
- learning
- multimodal data
- leaning analytics
