The nature of statistical learning theory
The nature of statistical learning theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
International Journal of Computer Vision
Gestures are strings: efficient online gesture spotting and classification using string matching
Proceedings of the ICST 2nd international conference on Body area networks
Fusion of String-Matched Templates for Continuous Activity Recognition
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Logical-shapelets: an expressive primitive for time series classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Action recognition using motion primitives and probabilistic edit distance
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
IEEE Transactions on Information Technology in Biomedicine
A feature selection-based framework for human activity recognition using wearable multimodal sensors
Proceedings of the 6th International Conference on Body Area Networks
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
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Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a "whole-motion" model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.