| Title |
The Analysis of Living Daily Activities by Interpreting Bi-Directional Accelerometer Signals with Extreme Learning Machine |
| Authors |
신항식(Shin, Hang-Sik) ; 이영범(Lee, Young-Bum) ; 이명호(Lee, Myoung-Ho) |
| Keywords |
Extreme Learning Machine ; Activity of Daily Living ; Accelerometer |
| Abstract |
In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20. |