Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Growing multi-dimensional self-organizing maps for motion detection
Self-Organizing neural networks
IEEE Transactions on Mobile Computing
Neural Networks
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
A mobile health and fitness companion demonstrator
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations Session
Aircraft engine health monitoring using self-organizing maps
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
IEEE Transactions on Mobile Computing
Personal and Ubiquitous Computing
Visualization for Activity Information Sharing System Using Self-Organizing Map
BWCCA '11 Proceedings of the 2011 International Conference on Broadband and Wireless Computing, Communication and Applications
IEEE Transactions on Information Technology in Biomedicine
Learning activity patterns using fuzzy self-organizing neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Comprehensive Survey of Wireless Body Area Networks
Journal of Medical Systems
Hierarchical overlapped SOM's for pattern classification
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
The evolving tree-analysis and applications
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Patient's motion recognition is quite popular in the area of healthcare and medical service nowadays. By analyzing the data from variant sensors within the network, we can estimate the activities a person does. The analyzing job is usually done by a classifier which can classify each motion into one category with similar movements. Self-Organizing Map (SOM) is a kind of algorithm that can be used to arrange data into different categories without any guidance. Decision tree is a mature tool for classification. In this paper, we propose a new kind of classification method with data from BAN called SOM-Decision Tree. Firstly, we use SOM on each of the sensor nodes to categorize motions into different classes, so that motions in different classes can be distinguished by this sensor. Secondly, a decision tree is constructed to discriminate each kind of movements from other motions. Finally, any action of the same patient can be recognized by query through the decision tree. According to our experiment, this algorithm is feasible and quite efficient.