Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Wearable Computing Meets Ubiquitous Computing: Reaping the Best of Both Worlds
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Using Multiple Sensors for Mobile Sign Language Recognition
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Gaussian process classification for segmenting and annotating sequences
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
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
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Recognition of hand movements using wearable accelerometers
Journal of Ambient Intelligence and Smart Environments
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
Proceedings of the 2nd Conference on Wireless Health
Recognition of hand movements using wearable accelerometers
Journal of Ambient Intelligence and Smart Environments
Parameter exploration for response time reduction in accelerometer-based activity recognition
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
| Hi-index | 0.00 |
This paper describes daily life activity recognition using wearable acceleration sensors attached to four different parts of the human body. The experimental data set consisted of signals recorded from 13 different subjects performing 17 daily activities. Furthermore, to attain more general activities, some of the most specific classes were combined for a total of 9 different activities. Simple time domain features were calculated from each sensor device. For the recognition task, we propose a novel sequential learning method that combines discriminative learning of individual input-output mappings using support vector machines (SVM) with generative learning to smooth temporal time-dependent activity sequences with a trained hidden Markov model (HMM) type transition probability matrix. The experiments show that the accuracy of the proposed method is superior to various conventional discriminative and generative methods alone, and it achieved a total recognition rate of 94% and 96% studying 17 and 9 different daily activities, respectively.