MavHome: An Agent-Based Smart Home
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
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
Recognition-based gesture spotting in video games
Pattern Recognition Letters
A general method for human activity recognition in video
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Depth silhouettes for gesture recognition
Pattern Recognition Letters
Shape-Based Human Activity Recognition Using Independent Component Analysis and Hidden Markov Model
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
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In this work, we propose a Human Activity Prediction (HAP) system using activity sequence spanning trees constructed from a life-log created by a video sensor-based daily Human Activity Recognition (HAR) system using time-sequential Independent Component (IC)-based depth silhouette features with Hidden Markov Models (HMMs). In the daily HAR system, the IC features are extracted from the collection of the depth silhouettes containing various daily human activities such as walking, sitting, lying, cooking, eating etc. Using these features, HMMs are used to model the time sequential features and recognize various human activities. The depth silhouette-based human activity recognition system is used to recognize daily human activities automatically in real time, which creates a life-log of daily activity events. In this work, we propose a method for human activity prediction using fixed-length activity sequence spanning trees based on the life-log. Utilizing the consecutive activities recorded in an activity sequence database (i.e. life-log) for a specific period of time of each day over a period such as a month, the fixed-length spanning trees can be constructed for the sequences starting with each activity where the leaf nodes contain the frequency of the fixed-length consecutive activity sequences. Once the trees are constructed, to predict an activity after a sequence of activities, we traverse the spanning trees until a path up to the previous node of the leaf nodes is matched with the testing pattern. Finally, we can predict the next activity based on the highest frequency of the leaf nodes along the matched path. The prediction experiments over the computer simulated data which is based on the daily logs show satisfactory results. Our video sensor-based human activity recognition and prediction systems can be utilized for practical applications such as smart and proactive healthcare.