SignificanceWe present a motion-resistant three-wavelength spatial frequency domain imaging (SFDI) system with ambient light suppression using an 8-tap complementary metal-oxide semiconductor (CMOS) image sensor (CIS) developed at Shizuoka University. The system addresses limitations in conventional SFDI systems, enabling reliable measurements in challenging imaging scenarios that are closer to real-world conditions.AimOur study demonstrates a three-wavelength SFDI system based on an 8-tap CIS. We demonstrate and evaluate the system’s capability of mitigating motion artifacts and ambient light bias through tissue phantom reflectance experiments and in vivo volar forearm experiments.ApproachWe incorporated the Hilbert transform to reduce the required number of projected patterns per wavelength from three to two per spatial frequency. The 8-tap image sensor has eight charge storage diodes per pixel; therefore, simultaneous image acquisition of eight images based on multi-exposure is possible. Taking advantage of this feature, the sensor simultaneously acquires images for planar illumination, sinusoidal pattern projection at three wavelengths, and ambient light. The ambient light bias is eliminated by subtracting the ambient light image from the others. Motion artifacts are suppressed by reducing the exposure and projection time for each pattern while maintaining sufficient signal levels by repeating the exposure. The system is compared to a conventional SFDI system in tissue phantom experiments and then in vivo measurements of human volar forearms.ResultsThe 8-tap image sensor-based SFDI system achieved an acquisition rate of 9.4 frame sets per second, with three repeated exposures during each accumulation period. The diffuse reflectance maps of three different tissue phantoms using the conventional SFDI system and the 8-tap image sensor-based SFDI system showed good agreement except for high scattering phantoms. For the in vivo volar forearm measurements, our system successfully measured total hemoglobin concentration, tissue oxygen saturation, and reduced scattering coefficient maps of the subject during motion (16.5 cm/s) and under ambient light (28.9 lx), exhibiting fewer motion artifacts compared with the conventional SFDI.ConclusionsWe demonstrated the potential for motion-resistant three-wavelength SFDI system with ambient light suppression using an 8-tap CIS.
We demonstrate a motion-resistant, three-wavelength, spatial frequency domain imaging (SFDI) system with ambient light suppression using a new 8-tap CMOS image sensor developed in our laboratory. Compared to the previous sensor (134×150), the new sensor’s readout maximum frame rate has improved to 33fps from 6.28fps, and the new 700×540- pixel sensor allows imaging at a higher spatial resolution over a larger field of view. Furthermore, the number of projected images needed per wavelength is reduced from three to two after applying the Hilbert transform. One image of planar illumination and one image for sinusoidal pattern projection at three wavelengths as well as one image of ambient light are captured by the 8-tap image sensor concurrently. The bias caused by ambient light is removed by subtracting the ambient light image from other images. Suppression of motion artifacts is achieved by reducing the exposure and projection time of each pattern. Sufficient signal level is maintained by repeating the exposure multiple times. In this study, LEDs with wavelengths of 554nm, 660nm, and 730nm were used to estimate oxy-/deoxyhemoglobin and melanin concentrations from in-vivo volar forearm skin.
Traffic accidents and mental stress are strongly correlated. Drivers under pressure are more easily to cause accidents. A system which could describe the mental state of a driver would be helpful to avoid such accidents. Multiple indices derived from analysis of heart rate variability (HRV) could be used in the estimation of mental state in humans; moreover, recent years, methods of non-contact heart rate estimation have been well studied and reached high accuracy. Based on both, we developed a real-time driver monitoring system which could not only estimate heart rate of the driver, but also indicate whether he is under pressure or not. This system delivers 2 outputs: heart rate(HR) and mental stress level (stress index). We utilized an 18-bit camera to grab frontal facial frames and independent component analysis (ICA) to extract haemoglobin signal from each frame. After temporal filtering and peak detection, R-R interval(RRI) will be obtained and HR measured. Mental stress estimation will start 30 seconds after we get the first RRI data, then a power spectrum analysis method will be applied to all of the HRV data within 30 seconds to generate powers of Low-Frequency and High-Frequency band data. The ratio of the powers in both bands, so called LF-HF ratio (LF/HF), will be delivered as a stress index to quantify the degree of mental stress. Finally, the validity of stress index is verified over arithmetic calculation and a number of driving-simulating scenarios.
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