SpO2 (Finger Clip)

This sensor can be used to estimate the oxygen saturation level in the blood with +/- 2% accuracy compared to a medical sensor.

This biosignalsplux SpO2 sensor is designed for oxygen saturation level estimations in the finger using two LED’s, one in the red region and the other in the infrared region of the spectrum. The reflected light of each one of these LED’s is absorbed by a photodiode that converts this current into a digital value, from which the blood oxygen saturation can be derived.

The finger clip form-factor enables a user-friendly application on the finger (commonly the index finger), without any additional accessories being required for the use of this sensor. The individual intensities of the LEDs can be controlled both using the OpenSignals (r)evolution software or the biosignalsplux APIs, enabling a location-specific light intensity configuration of this sensor.

Note: This sensor is also available in a versatile form-factor.

  • Adjustable LED intensities
  • Subtracts ambient light (reduces interference)
  • Pre-conditioned digital output
  • High signal-to-noise ratio
  • Ready-to-use form-factor
  • Medical-grade raw data output
Dual LED Design 1 red & 1 infrared LED
Red LED wavelength 660nm
Infrared wavelength 950nm
Connector Type UC-E6 (Male)
Detector Sensitivity 400nm-1100nm (max@920nm)
Resolution 16-bit
Sampling Frequency 500Hz
Cable Length 100cm±0.5cm (customizable)

This sensor is not a standalone use sensor and requires the use of a biosignalsplux hub (4 or 8 channel). It can be used with the following biosignalsplux kits and systems for data acquisition:

R. Fonseca-Pinto, N. Lopes, G. Brito, M. Lages, M. Guarino, Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology, in Health and Technology, vol. 10, pp. 79-85, 2020

L. Shu, J. Xie, M. Yang, Z. Li, D. Liao, X. Xu, X. Yang, A review of Emotion Recognition Using Phyisological Signals, in Sensors, vol. 18, no. 2074, pp. 1-41, 2018

J. Pagán, R. Fallahzadeh, H. Ghasemzadeh, J. Moya, J. Risco-Martín, J. Ayala, An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices, in Design, Automation & Test in Europe Conference & Exhibitation, pp. 1297-1302, 2017

J. Pagán, José L. Risco-Martín, José M. Moya, José L. Ayala, Grammatical Evolutionary Techniques for Prompt Migraine Prediction., in GECCO '16: roceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 973-980, 2016

Looking for more?

Visit the biosignalsplux publications page for a full list of available publications.

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