In the study of embryo development process, the morphological features at different stages are essential to evaluate developmental competence of the embryo, which can be used to optimize and improve the system for in-vitro embryo culture. In this paper, an online monitoring system was designed for long-term culture of embryos, based on a monitoring strategy of low-magnification search and high-magnification observation. Three optical modules of 4× phase contrast, 10× and 20× Hoffman modulation phase contrast were configured in this system to meet the requirements of different fields of view, especially when the size of the embryo increases during the culture. Using an optomechanical system matching design, an error control and alignment test, the resolution of optical imaging was guaranteed, and a relief stereoscopic imaging with high contrast of embryos was obtained. Through low-magnification field of view to identify and locate embryos and high-magnification field of view to capture the details, the system realized online tracking and monitoring of embryos. In addition, we developed and verified an embryo identifying and locating algorithm based on image contour area and definition evaluation. The online monitoring system of in-vitro embryo culture proposed in this paper can track and record the morphological features of embryos without affecting the embryo development, providing a basis for the assessment of embryo development and the optimization of in-vitro culture system.
The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson’s disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson’s disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.