Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

A collaborative robot for COVID-19 oropharyngeal swabbing.

The coronavirus disease 2019 (COVID-19) outbreak has increased mortality and morbidity world-wide. Oropharyngeal swabbing is a well-known and commonly used sampling technique for COVID-19 diagnose around the world. We developed a robot to assist with COVID-19 oropharyngeal swabbing to prevent frontline clinical staff from being infected. The robot integrates a UR5 manipulator, rigid-flexible coupling (RFC) manipulator, force-sensing and control subsystem, visual subsystem and haptic device. The robot has strength in intrinsically safe and high repeat positioning accuracy. In addition, we also achieve one-dimensional constant force control in the automatic scheme (AS). Compared with the rigid sampling robot, the developed robot can perform the oropharyngeal swabbing procedure more safely and gently, reducing risk. Alternatively, a novel robot control schemes called collaborative manipulation scheme (CMS) which combines a automatic phase and teleoperation phase is proposed. At last, comparative experiments of three schemes were conducted, including CMS, AS, and teleoperation scheme (TS). The experimental results shows that CMS obtained the highest score according to the evaluation equation. CMS has the excellent performance in quality, experience and adaption. Therefore, the proposal of CMS is meaningful which is more suitable for robot-sampling.

A scalable, secure, and interoperable platform for deep data-driven health management.

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

Design and Control of a Highly Redundant Rigid-flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling.

The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

The Diagnostic Value of Multi-Index Combined Detection in Patients with COVID-19.

BACKGROUND: To investigate the clinical value of multi-index combined detection in the diagnosis of new coronavirus disease 2019 (COVID-19). METHODS: A total of 63 laboratory confirmed patients treated in our hospital were selected as the COVID-19 group, including 28 severe patients and 35 non-severe patients. Another 50 healthy subjects undergoing physical examination simultaneously were selected as the healthy group. Here we performed a study on the laboratory characteristics and explored their efficacy for diagnosis of the disease. RESULTS: Compared with healthy people, the abnormal indicators of patients with COVID-19 are low levels of lymphocytes (LYM), red blood cells (RBC), hemoglobin (HGB), platelets (PLT), total protein (TP), and albumin (ALB), and high levels of monocytes (MON), aspartate aminotransferase (AST), gamma glutamyl transpeptidase (GGT), and C-reactive protein (CRP). The level of MON and CRP in severe patients were significantly increased compared with non-severe pneumonia patients, and indicators such as LYM and ALB were significantly reduced (p < 0.05). The sensitivity and specificity of the combined detection of LYM, MON, RBC, HGB, PLT, TP, ALB, AST, GGT, and CRP was 97.7% and 91.7%, which was higher than the single item (p < 0.05). The sensitivity and specificity of combined detection of LYM, MON, ALB, and CRP to predict the severity of COVID-19 were 96.4% and 73.0%, which were higher than those of separate detections (p < 0.05). CONCLUSIONS: The index of LYM, MON, RBC, HGB, PLT, TP, ALB, AST, GGT, and CRP can be used for the diagnosis of new COVID-19, and the indicators of LYM, MON, ALB, and CRP may be predictors of severe pneumonia. The combined detection of the laboratory indexes can diagnose COVID-19 and predict the severity more effectively and accurately.