Multi-sensor context-awareness in mobile devices and smart artifacts
Mobile Networks and Applications
Data Paths in Wearable Communication Networks
ARCS '02 Proceedings of the International Conference on Architecture of Computing Systems: Trends in Network and Pervasive Computing
Wearable sensing to annotate meeting recordings
Personal and Ubiquitous Computing
The case for reconfigurable hardware in wearable computing
Personal and Ubiquitous Computing
Sensing and Monitoring Professional Skiers
IEEE Pervasive Computing
Recognizing context for annotating a live life recording
Personal and Ubiquitous Computing - Memory and Sharing of Experiences
The ECORA framework: A hybrid architecture for context-oriented pervasive computing
Pervasive and Mobile Computing
Comparison of human and machine recognition of everyday human actions
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Navigation of mobile robots in unstructured environment using grid based fuzzy maps
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
UCS'04 Proceedings of the Second international conference on Ubiquitous Computing Systems
Tool use as gesture: new challenges for maintenance and rehabilitation
BCS '10 Proceedings of the 24th BCS Interaction Specialist Group Conference
An approach to social recommendation for context-aware mobile services
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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Much research has been conducted that uses sensor-based modules with dedicated software to automatically distinguish the user's situation or context. The best results were obtained when powerful sensors (such as cameras or GPS systems) and/or sensor-specific algorithms (like sound analysis) were applied. A somewhat new approach is to replace the one smart sensor by many simple sensors. We argue that neural networks are ideal algorithms to analyze the data coming from these sensors and describe how we came to one specific algorithm that gives good results, by giving an overview of several requirements. Finally, wearable implementations are given to show the feasibility and benefits of this approach and its implications.