C4.5: programs for machine learning
C4.5: programs for machine learning
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
A Temporal Object-Oriented Data Model with Multiple Granularities
TIME '99 Proceedings of the Sixth International Workshop on Temporal Representation and Reasoning
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
Augmenting and sharing memory with eyeBlog
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Next-Generation Personal Memory Aids
BT Technology Journal
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
InSense: Interest-Based Life Logging
IEEE MultiMedia
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
An activity monitoring system for elderly care using generative and discriminative models
Personal and Ubiquitous Computing
Activity recognition based on RFID object usage for smart mobile devices
Journal of Computer Science and Technology
GPARS: a general-purpose activity recognition system
Applied Intelligence
Situation recognition in sensor based environments using concept lattices
Proceedings of the CUBE International Information Technology Conference
Situation recognition: an evolving problem for heterogeneous dynamic big multimedia data
Proceedings of the 20th ACM international conference on Multimedia
Physical activity recognition using multiple sensors embedded in a wearable device
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on embedded systems for interactive multimedia services (ES-IMS)
Using visual lifelogs to automatically characterize everyday activities
Information Sciences: an International Journal
From health-persona to societal health
Proceedings of the 22nd international conference on World Wide Web companion
EventShop: recognizing situations in web data streams
Proceedings of the 22nd international conference on World Wide Web companion
Hi-index | 0.00 |
Most people already use phones with myriad sensors that continuously generate data streams related to most aspects of their life. By detecting events in basic data streams and correlating and reasoning among them, it is possible to create a chronicle of personal life. We call it Personicle and use this to build individual Health Persona. Such Health Persona may then be used for understanding societal health as well as making decisions in emerging Social Life Networks. In this paper, we present a framework that collects, manages, and correlates personal data from heterogeneous data sources and detects events happening at personal level to build health persona. We use several data streams such as motion tracking, location tracking, activity level, and personal calendar data. We illustrate how two recognition algorithms based on Formal Concept Analysis and Decision Trees can be applied to Life Event detection problem. Also, we demonstrate the applicability of this framework on simulated data from Moves app, GPS, Nike fuel band, and Google calendar. We expect to soon have results for several individuals using real data streams from disparate wearable and smart phone sensors.