We have developed a novel method to measure human cardiac pulse at a distance. It is based on the information contained in the thermal signal emitted from major superficial vessels. This signal is acquired through a highly sensitive thermal imaging system. Temperature on the vessel is modulated by pulsative blood flow. To compute the frequency of modulation (pulse), we extract a line-based region along the vessel. Then, we apply fast Fourier transform (FFT) to individual points along this line of interest to capitalize on the pulse's thermal propagation effect. Finally, we use an adaptive estimation function on the average FFT outcome to quantify the pulse. We have carried out experiments on a data set of 34 subjects and compared the pulse computed from our thermal signal analysis method to concomitant ground-truth measurements obtained through a standard contact sensor (piezo-electric transducer). The performance of the new method ranges from 88.52% to 90.33% depending on the clarity of the vessel's thermal imprint. To the best of our knowledge, it is the first time that cardiac pulse has been measured several feet away from a subject with passive means.

No abstract

Background: Artificial intelligence has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images. Aims and Objects: Use the model training of osteosarcoma as an example to illustrate the pitfalls of overfitting and how the addition of model input variability can help improve model performance. Materials and Methods: We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation. Results: The performance of the re-trained model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting. We show the additions of more and moresubtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances. Conclusions: In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.

In the present paper, we propose a new pulse measurement methodology based on thermal imaging (contact-free). The method capitalizes both on the thermal undulation produced by the traveling pulse as well as the periodic expansion of the compliant vessel wall. The paper reports experiments on 34 subjects, where it compares the performance of the new pulse measurement method to the one we reported previously. The measurements were ground-truthed through a piezo-electric sensor. Statistical analysis reveals that the new imaging methodology is more accurate and robust than the previous one. Its performance becomes nearly perfect, when the vessel is not obstructed by a thick fat deposit.

No abstract