A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
A review of feature selection techniques in bioinformatics
Bioinformatics
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sensor Placement for Activity Detection Using Wearable Accelerometers
BSN '10 Proceedings of the 2010 International Conference on Body Sensor Networks
IEEE Transactions on Information Technology in Biomedicine
Motion primitive-based human activity recognition using a bag-of-features approach
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Feature learning for detection and prediction of freezing of gait in parkinson's disease
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Cost-effective activity recognition on mobile devices
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
| Hi-index | 0.00 |
Human activity recognition is important for many applications. This paper describes a human activity recognition framework based on feature selection techniques. The objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.