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This paper describes RTBioT, one of the first Internet of Things (IoT) healthcare platforms based on spatially resolved near infrared (NIR) spectroscopy to support non-invasively quantify chromophores in biological tissue. Bluetooth Low Energy (BLE) is used as the primary communication protocol, an IR-enhanced Si PIN photodiode is for a light-receiving element, and a compact fiber-stub type beam combiner is employed as a multiple wavelengths light-emitting source. Most of all, a lock-in amplifier is to retrieve the low noise signal from photodiode which enables accurate measurement of small modulated signals in the presence of noise interference orders of magnitude greater than the signal amplitude by using phase-sensitive detection technique (PSD). The sampling rate of the RTBioT is up to 33Hz, so that it can directly measure mayer wave oscillation, respiration, and cardiac cycle from the raw data. However, it is necessary to approach to the statistical analysis to quantify the concentration of tissue chromophores. First, we determine the optical absorption and scattering properties in the tissue from the locked-in received signal by using the algorithm composed of least square method and diffusion equation. Then, inverse-matrix equation with absorption, reduced scattering and extinction coefficients is solved by the algorithm with respect to chromophores. We conducted an experiment through phantoms simulating human tissue and human subjects to demonstrate its feasibility for the IoT healthcare platform. The experimental results show that it is possible to monitor the biological signals and the concentrations of chromophores in a human subject in near real time fashion.
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