Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.

PrISM-Tracker: A Framework for Multimodal Procedure Tracking UsingWearable Sensors and State Transition Information with User-Driven Handling of Errors and Uncertainty

A user often needs training and guidance while performing several daily life procedures, e.g., cooking, setting up a new appliance, or doing a COVID test. Watch-based human activity recognition (HAR) can track users' actions during these procedures. However, out of the box, state-of-the-art HAR struggles from noisy data and less-expressive actions that are often part of daily life tasks. This paper proposes PrISM-Tracker, a procedure-tracking framework that augments existing HAR models with (1) graph-based procedure representation and (2) a user-interaction module to handle model uncertainty. Specifically, PrISM-Tracker extends a Viterbi algorithm to update state probabilities based on time-series HAR outputs by leveraging the graph representation that embeds time information as prior. Moreover, the model identifies moments or classes of uncertainty and asks the user for guidance to improve tracking accuracy. We tested PrISM-Tracker in two procedures: latte-making in an engineering lab study and wound care for skin cancer patients at a clinic. The results showed the effectiveness of the proposed algorithm utilizing transition graphs in tracking steps and the efficacy of using simulated human input to enhance performance. This work is the first step toward human-in-the-loop intelligent systems for guiding users while performing new and complicated procedural tasks. © 2023 Owner/Author.

SNIF-DFA: A Signal Processing and Information Fusion Method for Smart Gua Sha Device

Due to the COVID-19 global pandemic, there are more needs for remote patient care especially in rehabilitation requiring direct contact. However, traditional Chinese rehabilitation technologies, such as gua sha, often need to be implemented by well-trained professionals. To automate and professionalize gua sha, it is necessary to record the nursing and rehabilitation process and reproduce the process in developing smart gua sha equipment. This paper proposes a new signal processing and sensor fusion method for developing a piece of smart gua sha equipment. A novel stabilized numerical integration method based on information fusion and detrended fluctuation analysis (SNIF-DFA) is performed to obtain the velocity and displacement information during gua sha operation. The experimental results show that the proposed method outperforms the traditional numerical integration method with respect to information accuracy and realizes accurate position calculations. This is of great significance in developing robots or automated machines that reproduce the nursing and rehabilitation operations of medical professionals. IEEE