What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
ISWC '98 Proceedings of the 2nd IEEE International Symposium on Wearable Computers
Context-based video retrieval system for the life-log applications
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
A Life-Log Search Model Based on Bayesian Network
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Do life-logging technologies support memory for the past?: an experimental study using sensecam
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GeoLife: Managing and Understanding Your Past Life over Maps
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Modeling and Analyzing Individual's Daily Activities using Lifelog
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
BeTelGeuse: A Platform for Gathering and Processing Situational Data
IEEE Pervasive Computing
SIMACT: a 3D open source smart home simulator for activity recognition
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Exploiting mobile contexts for Petri-net to generate a story in cartoons
Applied Intelligence
A life log collector integrated with a remote-controller for enabling user centric services
IEEE Transactions on Consumer Electronics
Improving fault tolerance of wearable wearable sensor-based activity recognition techniques
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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Recording a personal life log (PLL) of daily activities in a ubiquitous environment is an emerging application of information technology. In this work, we present a single tri-axial accelerometer-based PLL system capable of human activity recognition and exercise information generation. Our PLL system exhibits two main functions: activity recognition and exercise information generation. For activity recognition, the system first recognizes a state of daily activities based on the statistical and spectral features of the accelerometer signals. An activity within the recognized state is then recognized using a set of augmented features, including autoregressive coefficients, signal magnitude area, and tilt angle, via linear discriminant analysis and hierarchical artificial neural networks. Upon the recognition of each activity, the system further estimates exercise information that includes energy expenditure based on metabolic equivalents, stride length, step count, walking distance, and walking speed. Our PLL system operates in real-time, and the life log information it generates is archived in a daily log database. We have validated our PLL system for six daily activities (i.e., lying, standing, walking, going-upstairs, going-downstairs, and driving) via subject-independent and subject-dependent recognition on a total of twenty subjects, achieving an average recognition accuracy of 94.43 and 96.61%, respectively. Our results demonstrate the feasibility of a portable real-time PLL system that could be used for u-lifecare and u-healthcare services in the near future.