Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Inferring definite-clause grammars to express multivariate time series
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM
IEICE - Transactions on Information and Systems
Proceedings of the 6th international conference on Mobile systems, applications, and services
Analysis of body sensor network using human body as the channel
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
MSP430 Microcontroller Basics
Human activity recognition with action primitives
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
A methodology for extracting temporal properties from sensor network data streams
Proceedings of the 7th international conference on Mobile systems, applications, and services
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
IEEE Journal on Selected Areas in Communications - Special issue on body area networking: Technology and applications
Multi-represented classification based on confidence estimation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Action recognition using motion primitives and probabilistic edit distance
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Graph-Based multiple classifier systems a data level fusion approach
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A mining technique using n-grams and motion transcripts for body sensor network data repository
WH '10 Wireless Health 2010
A bag-of-features-based framework for human activity representation and recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Motion primitive-based human activity recognition using a bag-of-features approach
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design
Behavior-oriented data resource management in medical sensing systems
ACM Transactions on Sensor Networks (TOSN)
A method for cricket bowling action classification and analysis using a system of inertial sensors
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Journal of Mobile Multimedia
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Body sensor networks are emerging as a promising platform for remote human monitoring. With the aim of extracting bio-kinematic parameters from distributed body-worn sensors, these systems require collaboration of sensor nodes to obtain relevant information from an overwhelmingly large volume of data. Clearly, efficient data reduction techniques and distributed signal processing algorithms are needed. In this paper, we present a data processing technique that constructs motion transcripts from inertial sensors and identifies human movements by taking collaboration between the nodes into consideration. Transcripts of basic motions, called primitives, are built to reduce the complexity of the sensor data. This model leads to a distributed algorithm for segmentation and action recognition. We demonstrate the effectiveness of our framework using data collected from five normal subjects performing ten transitional movements. The results clearly illustrate the effectiveness of our framework. In particular, we obtain a classification accuracy of 84.13% with only one sensor node involved in the classification process.