Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
A Mobile Phone-based Wearable Vital Signs Monitoring System
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Living assistance systems: an ambient intelligence approach
Proceedings of the 28th international conference on Software engineering
Accelerometer-based gesture control for a design environment
Personal and Ubiquitous Computing
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
An activity recognition system for mobile phones
Mobile Networks and Applications
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
uWave: Accelerometer-based personalized gesture recognition and its applications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Object relevance weight pattern mining for activity recognition and segmentation
Pervasive and Mobile Computing
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
The application of machine-learning on lower limb motion analysis in human exoskeleton system
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Pattern-based real-time feedback for a temporal bone simulator
Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology
RFID shakables: pairing radio-frequency identification tags with the help of gesture recognition
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
A computing-efficient algorithm for accelerometer-based real-time activity recognition systems
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Activity recognition on streaming sensor data
Pervasive and Mobile Computing
Learning a taxonomy of predefined and discovered activity patterns
Journal of Ambient Intelligence and Smart Environments
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Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network's communication cost by 60.2%.