The familiar: a living diary and companion
CHI '01 Extended Abstracts on Human Factors in Computing Systems
MyLifeBits: fulfilling the Memex vision
Proceedings of the tenth ACM international conference on Multimedia
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Computational Auditory Scene Analysis and Its Application to Robot Audition
ICKS '04 Proceedings of the International Conference on Informatics Research for Development of Knowledge Society Infrastructure
Next-Generation Personal Memory Aids
BT Technology Journal
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
InSense: Interest-Based Life Logging
IEEE MultiMedia
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Unsupervised clustering of ambulatory audio and video
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Bathroom activity monitoring based on sound
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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Virtual worlds like “Second Life” are popular graphical representations of real (and fictitious) places, which are inhabited by real people in the form of personal avatars. The existence of people in these worlds is either (1) as avatars manipulated by users (to make them walk, fly, chat, etc), or (2) as pre-scripted agents, called “bots”, which are programmed to display some predefined behavior in the virtual world. Research that aims to bridge real life and these virtual worlds to simulate virtual living, while challenging and promising, is currently rare. Only very recently the mapping of real-world activities to virtual worlds has been attempted by processing multiple sensors data along with inference logic for real-world activities. Detecting or inferring human activity using such simple sensor data is often inaccurate and insufficient. Hence, this paper explains to infer human activity from environmental sound cues and common sense knowledge, which is an inexpensive alternative to other sensors (e.g., accelerometers). We discuss the challenges to implement such a system from the signal processing and agent based system point of view. To the best of our knowledge, this system pioneers the use of environmental sound based activity recognition in mobile computing to reflect one’s real-world activity in virtual worlds.