In this paper, we propose a smartphone-based biosensor for detecting human total hemoglobin concentration in vivo with high accuracy. Compared to the existing biosensors used to measure hemoglobin concentration, the smartphone-based sensor utilizes the camera, memory, and computing power of the phone. Thus, the cost is largely reduced. Compared to existing smartphone-based sensors, we developed a highly integrated multi-wavelength LED module and a specially designed phone fixture to reduce spatial errors and motion artifacts, respectively. In addition, we embedded a new algorithm into our smartphone-based sensor to improve the measurement accuracy; an L*a*b* color space transformation and the “a” parameter were used to perform the final quantification. We collected 24 blood samples from normal and anemic populations. The adjusted R² of the prediction results obtained from the multiple linear regression method reached 0.880, and the RMSE reached 9.04, which met the accuracy requirements of non-invasive detection of hemoglobin concentration.

The characterization and evaluation of skin tissue structures are crucial for dermatological applications. Recently, Mueller matrix polarimetry and second harmonic generation microscopy have been widely used in skin tissue imaging due to their unique advantages. However, the features of layered skin tissue structures are too complicated to use a single imaging modality for achieving a comprehensive evaluation. In this study, we propose a dual-modality imaging method combining Mueller matrix polarimetry and second harmonic generation microscopy for quantitative characterization of skin tissue structures. It is demonstrated that the dual-modality method can well divide the mouse tail skin tissue specimens’ images into three layers of stratum corneum, epidermis, and dermis. Then, to quantitatively analyze the structural features of different skin layers, the gray level co-occurrence matrix is adopted to provide various evaluating parameters after the image segmentations. Finally, to quantitatively measure the structural differences between damaged and normal skin areas, an index named Q-Health is defined based on cosine similarity and the gray-level co-occurrence matrix parameters of imaging results. The experiments confirm the effectiveness of the dual-modality imaging parameters for skin tissue structure discrimination and assessment. It shows the potential of the proposed method for dermatological practices and lays the foundation for further, in-depth evaluation of the health status of human skin.