Multichannel Symbolic Aggregate Approximation Intelligent Icons: Application for Activity Recognition
- Resource Type
- Conference
- Authors
- Pappa, Lamprini; Karvelis, Petros; Georgoulas, George; Stylios, Chrysostomos
- Source
- 2020 IEEE Symposium Series on Computational Intelligence (SSCI) Computational Intelligence (SSCI), 2020 IEEE Symposium Series on. :505-512 Dec, 2020
- Subject
- Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Time series analysis
Accelerometers
Sensors
Aggregates
Gyroscopes
Machine learning algorithms
Telecommunications
Human Activity Recognition
Symbolic Aggregate Approximation
Multichannel Intelligent Icons
Classification
- Language
In this work, we introduce the Multichannel Intelligent Icons, a novel method for producing and presenting essential patterns of multidimensional bio-signals. The proposed approach is an extension of Symbolic Aggregate Approximation (SAX) along with an innovative variation of Intelligent Icons. The innovation on the approach stands on the grounds of creating a spatial correlation of the inherited information in all dimensions and so it provides extra features for distinguishing the human activities. The proposed model is testing on Human Activity recorded data and for the classification purposes a Nearest Neighbour classifier is applied. The achieved results are compared with the case of applying single-channel intelligent icons approach and it is inferred a noteworthy increase in terms of accuracy and sensitivity with the proposed approach.