Graphical models for recognizing human interactions
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
Using Multiple Sensors for Mobile Sign Language Recognition
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
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
Eco: Ultra-Wearable and Expandable Wireless Sensor Platform
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Closed form solutions for mapping general distributions to quasi-minimal PH distributions
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Garment-based body sensing using foam sensors
AUIC '06 Proceedings of the 7th Australasian User interface conference - Volume 50
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
Gesture recognition with a Wii controller
Proceedings of the 2nd international conference on Tangible and embedded interaction
WUW - wear Ur world: a wearable gestural interface
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Gesture Recognition with a 3-D Accelerometer
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
uWave: Accelerometer-based personalized gesture recognition and its applications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Enabling always-available input with muscle-computer interfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Performance Analysis of an HMM-Based Gesture Recognition Using a Wristwatch Device
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
MAGIC: a motion gesture design tool
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GART: the gesture and activity recognition toolkit
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Active capacitive sensing: exploring a new wearable sensing modality for activity recognition
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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In this paper we demonstrate a Markov model based technique for recognizing gestures from accelerometers that explicitly represents duration. We do this by embedding an Erlang-Cox state transition model, which has been shown to accurately represent the first three moments of a general distribution, within a Dynamic Bayesian Network (DBN). The transition probabilities in the DBN can be learned via Expectation-Maximization or by using closed-form solutions. We test this modeling technique on 10 hours of data collected from accelerometers worn by babies pre-categorized as high-risk in the Newborn Intensive Care Unit (NICU) at UCI. We show that by treating instantaneous machine learning classification values as observations and explicitly modeling duration, we improve the recognition of Cramped Synchronized General Movements, a motion highly correlated with an eventual diagnosis of Cerebral Palsy.