Context Awareness by Analyzing Accelerometer Data
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Continuous lifelong capture of personal experience with EyeTap
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Efficient retrieval of life log based on context and content
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Passive capture and ensuing issues for a personal lifetime store
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Practical experience recording and indexing of Life Log video
CARPE '05 Proceedings of the 2nd ACM workshop on Continuous archival and retrieval of personal experiences
A language-based approach to indexing heterogeneous multimedia lifelog
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Restrain from pervasive logging employing geo-temporal policies
Proceedings of the 10th asia pacific conference on Computer human interaction
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In this paper, we propose new system for storing and retrieval of personal life log media on ubiquitous environment. We can gather personal life log media from intelligent gadgets which are connected with wireless network. Our intelligent gadgets consist of wearable gadgets and environment gadgets. Wearable gadgets include audiovisual device, GPS, 3D-accelerometer and physiological reaction sensors. Environment gadgets include the smart sensors attached to the daily supplies, such as cup, chair, door and so on. User can get multimedia stream with wearable intelligent gadget and also get the environmental information around him from environment gadgets as personal life log media. These life log media(LLM) can be logged on the LLM server in realtime. In LLM server, learning-based activity analysis engine will process logged data and create meta data for retrieval automatically. By using proposed system, user can log with personalized life log media and can retrieve the media at any time. To give more intuitive retrieval, we provide intuitive spatiotemporal graphical user interface in client part. Finally we can provide user-centered service with individual activity registration and classification for each user with our proposed system.