Characterization Speckle Effect on Measurement of Blood Flow Using Sensor Based on Self-Mixing Interferometry
| Main Authors: | Dzulfikar, Ahmad Zaki; Department of Physics, Institut Teknologi Sepuluh Nopember, Rubiyanto, Agus; Department of Physics, Institut Teknologi Sepuluh Nopember, Endarko, Endarko; Department of Physics, Institut Teknologi Sepuluh Nopember |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Institut Teknologi Sepuluh Nopember
, 2019
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| Subjects: | |
| Online Access: |
http://iptek.its.ac.id/index.php/jps/article/view/5116 http://iptek.its.ac.id/index.php/jps/article/view/5116/3500 |
Daftar Isi:
- The applications of Self-Mixing Interferometry (SMI) have been popular in many fields, including biomedical signals. The self-mixing effect occurs from the coherent back-coupling of the reflected or scattered lights from a target surface. The reflected lights will be detected by a photodiode which has been integrated in one device with the laser. That's why the SMI sensor is quite practical, affordable and simple. However, SMI has the serious problem with the presence of speckle effect in measured signal. The speckle effect produced by the human tissue is called “biospeckles.” The biospeckles observed from the skin tissues contain information about the blood flow in dermal capillarities, heartbeat, and others. These biospeckle patterns cause random modulations that will be detected as random amplitude and spectrum by photodiode. In this paper we present a technique to characterize speckle effect on measurement of blood flow in fingertip using sensor based on Self-Mixing Interferometry (SMI). We used a laser diode 785 nm as a light source and a constant current of 70 mA as a current source which is irradiated on the skin tissue in the fingertip. Then, the backscattered light reenters the laser cavity and it will be detected by photodiode. The SMI signal with speckle effect will be processed by Continuous Wavelet Transform for reconstruction and detection fringe. Signal processing results show that the number of detected speckle fringes depends largely on determining the number of wavelet waves and the scale used. The fringe pattern resulting from the reconstruction of the signal can be used to determine the frequency of speckles due to object movement. The average speckle frequency of fingertip is 0,5-0,7 Hz