Comparison of time- and frequency-domain features for movement classification using data from wrist-worn sensors
| Autor: | Zoltan Kincses, Peter Sarcevic, Szilveszter Pletl |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Computer science business.industry 010401 analytical chemistry Feature extraction Pattern recognition 02 engineering and technology computer.software_genre 01 natural sciences 0104 chemical sciences Data acquisition Frequency domain Multilayer perceptron Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Wireless sensor network |
| Zdroj: | SISY |
| Popis: | Inertial and magnetic sensors are widely used for different pattern recognition applications. In this paper, features extracted using time- and frequency-domain analysis are compared for human movement classification. Applied data were collected using wrist-mounted Wireless Sensor Network (WSN) motes equipped with 9 degree of freedom (9DOF) sensor boards. Data acquisition was done with the help of multiple subjects. To explore the capabilities of the used sensor types, different feature sets were generated and tested using multiple sensor combinations, and the feature extraction was tested utilizing raw sensor signals and computed magnitudes. The classification was done using MultiLayer Perceptron (MLP) neural networks. The obtained results show that the time-domain features (TDFs) provide higher classification efficiencies than frequency-domain features (FDFs). The highest obtained classification rate on unknown data was 91.74% using TDFs, and 88.51% applying FDFs. |
| Databáze: | OpenAIRE |
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