C4.5: programs for machine learning
C4.5: programs for machine learning
Multi-sensor fusion: fundamentals and applications with software
Multi-sensor fusion: fundamentals and applications with software
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Computer
Using Gravity to Estimate Accelerometer Orientation
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Sensor-Based Abnormal Human-Activity Detection
IEEE Transactions on Knowledge and Data Engineering
Multi-sensor fusion for human daily activity recognition in robot-assisted living
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Multisensor Fusion for Monitoring Elderly Activities at Home
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Distributed recognition of human actions using wearable motion sensor networks
Journal of Ambient Intelligence and Smart Environments
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
Detecting Fall Risk Factors for Toddlers
IEEE Pervasive Computing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
In this paper, we describe and evaluate an activity recognition system using a single 3-axis accelerometer and a barometric sensor worn on a waist of the body. The purpose of this work is to prevent child accidents such as unintentional injuries at home. In order to prevent child accidents in the home and reduce efforts of parents, we present a new safety management system for babies and children. We collected labeled accelerometer data from babies as they performed daily activities which are standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, stopping, wiggling, and rolling. In order to recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. We used the resulting training data to induce a predictive model for activity recognition. Naive Bayes, Bayes Net, Support Vector Machine, k-Nearest Neighbor, Decision Tree, Decision Table, Multilayer Perceptron, Logistic classifiers are tested on these features. Classification results using training and eight classifiers were compared. The overall accuracy of activity recognition was 96.2% using only a single wearable triaxial accelerometer sensor with the k-Nearest Neighbor.