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Periodical changes in bioimpedance caused by blood perfusion are relatively fast and weak; therefore, a fast, high-precision imaging system is required. A novel electrical impedance tomography (EIT) system is proposed for real-time perfusion imaging. Synchronous parallel sampling is combined with timely excitation switching control to achieve fast acquisition and maintain high sampling accuracy. To further improve the overall precision of the system, a bipolar-mirror feedback voltage controlled current source and an internal calibration unit was used to improve the current precision and measurement accuracy, respectively. Test results of the resistor model show that the system has a signal-to-noise ratio of 81 dB at 50 kHz and achieves an imaging rate of 100 frames/ s. A preliminary test shows that the reconstructed EIT images reveal a complete cycle of blood exchange between the heart and lungs. Our system may provide a useful research platform for real-time and non-invasive blood perfusion imaging, which could be used in in vivo studies.

In this paper, a remote sensing method for heart rate (HR) detection is proposed via nearinfrared facial video data, which can be used for the online monitoring of the physiological parameters of the drivers and further analysis so as to enhance the driving safety. The proposed HR detection method is based on an automatic facial tracking algorithm, i.e., the Kanade-Lucas-Tomasi, to transform the facial images into time-series signals. Empirical mode decomposition, bandpass filtering and fast Fourier transformation are applied to the time-series signals for the HR extraction. Indoor experiments in different scenarios are conducted to discuss the involved impact factors, i.e., the distance between the near-infrared camera and the participant, the illumination intensity and the video duration, to acquire the optimal settings for HR measurement. It is more robust and convenient than those of the current commercial devices and webcambased HR measurement approaches, especially in complex illumination environments, such as wearing hat, glasses or makeup, with measurement accuracy 95%. Field experiments are also performed for the prognosis of physical state of the driver.

There are a large number of high-dimensional data in the Internet of Brain Things, and the data is reduced from the high-dimensional to low-dimensional to maintain the similarity between the data, thereby effectively ensuring the operating speed of the Internet of Brain Things. In the traditional method, the distributed dimensionality reduction reconstruction algorithm of the High dimensional data has poor dimensionality reduction effect and serious data distortion after reconstruction. A method for dimensionality reconstruction of high-dimensional data in the Internet of Brain Things is proposed. By using the algorithm of linear discriminant analysis, the projection matrix of high dimensional data is constructed to solve it. According to the solution results, using improved implicit variable model to establish a high dimensional large data dimensionality reduction model for the Internet of Brain Things. The fitness value of data after dimensionality reduction is calculated by quantum immune clonal algorithm, and the optimal individual and optimal solution are determined. The data reconfiguration is realized through the optimal solution marshalling. Experimental results show that the proposed algorithm can effectively improve the dimensionality reduction of High dimensional data in the Internet of Brain Things. After reconstruction, the reconstructed data retain accurate data information, the reliability of reconstructed data is high, and the computational complexity is not high, the need for small storage space, and the advantages of strong promotion ability.