Factored light-field (LF) technology helps resolving the vergence-accommodation conflict inherent to the most of conventional stereoscopic displays. The remaining challenges include decreasing computation cost of light-field factorization and improving image quality. We prototyped a dual-layer light-field stereoscope with a smartphone used as a display. We implement and compare three different methods of rank-one LF factorization and two ways of initializing them. The weighted rank-one residual iterations (WRRI) and the weighted nonnegative matrix factorization (WNMF) proved almost twice faster than Huang et al.’s method in our implementation. Our tests revealed that the best way of initialization for all the three methods is that by the square root of the LF central view values; namely, one-two iterations are enough to achieve acceptable image quality.
We propose multimodal sensor and algorithm for automatic recognition of a food intake based on glycemic response. Embedding this sensor in a wearable device makes it possible to count number of meals at a given time and to generate personalized statistical pattern of eating habits. This pattern may have significant impact on both personal health care and big-data-driven social engineering. We use near-infrared diffuse reflectance spectroscopy, bioimpedance measurements, and binary classification for non-invasive continuous glucose trend measurements and Fourier transform based time frequency analysis of glycose trends for characterization of eating patterns and prediction of digestive system abnormalities. We tested the sensor in a series of experiments with the certain type of food and achieved 45% average accuracy of a food intake recognition with the random noise level being at 25%.
Unhealthy nutrition trends determination technique is described. Combination of optical spectroscopy and electrical
impedancemetry will lead to development of a healthcare device that will predict unhealthy eating habits and decrease
risk factors of diseases development.