Share Email Print
cover

Proceedings Paper

Automatic body flexibility classification using laser doppler flowmeter
Author(s): I-Chan Lien; Yung-Hui Li; Jian-Guo Bau
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Body flexibility is an important indicator that can measure whether an individual is healthy or not. Traditionally, we need to prepare a protractor and the subject need to perform a pre-defined set of actions. The measurement takes place at the same time when the subject performs required action, which is clumsy and inconvenient. In this paper, we propose a statistical learning model using the technique of random forest. The proposed system can classify body flexibility based on LDF signals analyzed in the frequency domain. The reasons of using random forest are because of their efficiency (fast in classification), interpretable structures and their ability to filter out irrelevant features. In addition, using random forest can prevent the problem of over-fitting, and the output model will become more robust to noises. In our experiment, we use chirp Z-transform (CZT), to transform a LDF signal into its energy values in five frequency bands. Combining the power of the random forest algorithm and frequency band analysis methods, a maximum recognition rate of 66% is achieved. Compared to traditional flexibility measuring process, the proposed system shortens the long and tedious stages of measurement to a simple, fast and pre-defined activity set. The major contributions of our work include (1) a novel body flexibility classification scheme using non-invasive biomedical sensor; (2) a set of designed protocol which is easy to conduct and practice; (3) a high precision classification scheme which combines the power of spectrum analysis and machine learning algorithms.

Paper Details

Date Published: 15 October 2015
PDF: 5 pages
Proc. SPIE 9672, AOPC 2015: Advanced Display Technology; and Micro/Nano Optical Imaging Technologies and Applications, 96720L (15 October 2015); doi: 10.1117/12.2199503
Show Author Affiliations
I-Chan Lien, National Central Univ. (Taiwan)
Yung-Hui Li, National Central Univ. (Taiwan)
Jian-Guo Bau, Hungkuang Univ. (Taiwan)


Published in SPIE Proceedings Vol. 9672:
AOPC 2015: Advanced Display Technology; and Micro/Nano Optical Imaging Technologies and Applications
Byoungho Lee; Yikai Su; Min Gu; Xiaocong Yuan; Daniel Jaque, Editor(s)

© SPIE. Terms of Use
Back to Top