Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
BehaviorScope: Real-Time Remote Human Monitoring Using Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
An ontology based approach for activity recognition from video
MM '08 Proceedings of the 16th ACM international conference on Multimedia
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Learning situation models in a smart home
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A Hierarchical Rule-Based Activity Recognition System with Frequency Pattern Mining
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Activity recognition in smart homes: from specification to representation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
COSAR: hybrid reasoning for context-aware activity recognition
Personal and Ubiquitous Computing
Unsupervised discovery of structure in activity data using multiple eigenspaces
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Building reliable activity models using hierarchical shrinkage and mined ontology
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
A Knowledge-Driven Approach to Activity Recognition in Smart Homes
IEEE Transactions on Knowledge and Data Engineering
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
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In a dense sensor-based smart home (SH), a significant challenge is to segment the sensor data stream in real-time to continuously support progressive activity recognition (AR). In this paper, we evaluate an approach that supports the segmentation of the sensor data stream used for knowledge-driven AR. The approach is based on the notion of dynamically varied sliding time windows, where data segments are formally modeled as time windows and ontological reasoning used to infer the ongoing activities of daily living (ADLs). We then present an algorithm that supports perpetual, real-time activity recognition and provide an implementation of both the proposed approach and the ADL ontology it uses. For evaluation, we developed a synthetic data generator and generated a set of synthetic ADLs. In addition, we implemented a real-time activity recognition system and a simple simulator that plays back a synthetic ADL as if the sensors are activated in real-time. We evaluated the real-time activity recognition algorithm, and obtained 99.2% average recognition accuracy on the synthetic ADLs tested. The ability of the algorithm to discriminate sensors that are activated in error was evaluated for selected ADLs and impressive results obtained.