A Medical Assistive Robot for Telehealth Care During the COVID-19 Pandemic: Development and Usability Study in an Isolation Ward.

BACKGROUND: The COVID-19 pandemic is affecting the mental and emotional well-being of patients, family members, and health care workers. Patients in the isolation ward may have psychological problems due to long-term hospitalization, the development of the epidemic, and the inability to see their families. A medical assistive robot (MAR), acting as an intermediary of communication, can be deployed to address these mental pressures. OBJECTIVE: CareDo, a MAR with telepresence and teleoperation functions, was developed in this work for remote health care. The aim of this study was to investigate its practical performance in the isolation ward during the pandemic. METHODS: Two systems were integrated into the CareDo robot. For the telepresence system, a web real-time communications solution is used for the multiuser chat system and a convolutional neural network is used for expression recognition. For the teleoperation system, an incremental motion mapping method is used for operating the robot remotely. A clinical trial of this system was conducted at First Affiliated Hospital, Zhejiang University. RESULTS: During the clinical trials, tasks such as video chatting, emotion detection, and medical supplies delivery were performed via the CareDo robot. Seven voice commands were set for performing system wakeup, video chatting, and system exiting. Durations from 1 to 3 seconds of common commands were set to improve voice command detection. The facial expression was recorded 152 times for a patient in 1 day for the psychological intervention. The recognition accuracy reached 95% and 92.8% for happy and neutral expressions, respectively. CONCLUSIONS: Patients and health care workers can use this MAR in the isolation ward for telehealth care during the COVID-19 pandemic. This can be a useful approach to break the chains of virus transmission and can also be an effective way to conduct remote psychological intervention.

A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics

Predicting and managing the movement of people in a region during epidemics’ outbreak is an important step in preventing outbreaks. The protection of user privacy during the outbreak has become a matter of public concern in recent years, yet deep learning models based on datasets collected from mobile devices may pose privacy and security issues. Therefore, how to develop an accurate crowd flow prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. In this paper, we propose a privacy-preserving mobility prediction framework via federated learning (CFPF) to solve this problem without significantly sacrificing the prediction performance. In this framework, we designed a deep and embedding learning approach called “Multi-Factors CNN-LSTM” (MFCL) that can help to explicitly learn from human trajectory data (weather, holidays, temperature, and POI) during epidemics. Furthermore, we improve the existing federated learning framework by introducing a clustering algorithm to classify clients with similar spatio-temporal characteristics into the same cluster, and select servers at the center of the cluster as edge central servers to integrate the optimal model for each cluster and improve the prediction accuracy. To address the privacy concerns, we introduce local differential privacy into the FL framework which can facilitate collaborative learning with uploaded gradients from users instead of sharing users’ raw data. Finally, we conduct extensive experiments on a realistic crowd flow dataset to evaluate the performance of our CFPF and make a comparison with other existing models. The experimental results demonstrate that our solution can not only achieve accurate crowd flow prediction but also provide a strong privacy guarantee at the same time.

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics.

In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics. Among them, flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change. This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring. Four categories of humidity sensors are highlighted based on resistive, capacitive, impedance-type and voltage-type working mechanisms. Furthermore, typical strategies including chemical doping, structural design and Joule heating are introduced to enhance the performance of humidity sensors. Drawing on the noncontact perception capability, human/plant healthcare management, human-machine interactions as well as integrated humidity sensor-based feedback systems are presented. The burgeoning innovations in this research field will benefit human society, especially during the COVID-19 epidemic, where cross-infection should be averted and contactless sensation is highly desired.

GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic.

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

Review of Robot Skin: A Potential Enabler for Safe Collaboration, Immersive Teleoperation, and Affective Interaction of Future Collaborative Robots

The emerging applications of collaborative robots (cobots) are spilling out from product manufactories to service industries for human care, such as patient care for combating the coronavirus disease 2019 (COVID-19) pandemic and in-home care for coping with the aging society. There are urgent demands on equipping cobots with safe collaboration, immersive teleoperation, affective interaction, and other features (e.g., energy autonomy and self-learning) to make cobots capable of these application scenarios. Robot skin, as a potential enabler, is able to boost the development of cobots to address these distinguishing features from the perspective of multimodal sensing and self-contained actuation. This review introduces the potential applications of cobots for human care together with those demanded features. In addition, the explicit roles of robot skin in satisfying the escalating demands of those features on inherent safety, sensory feedback, natural interaction, and energy autonomy are analyzed. Furthermore, a comprehensive review of the recent progress in functionalized robot skin in components level, including proximity, pressure, temperature, sensory feedback, and stiffness tuning, is presented. Results show that the codesign of these sensing and actuation functionalities may enable robot skin to provide improved safety, intuitive feedback, and natural interfaces for future cobots in human care applications. Finally, open challenges and future directions in the real implementation of robot skin and its system synthesis are presented and discussed.

The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: a modelling study.

For controlling recent COVID-19 outbreaks around the world, many countries have implemented suppression and mitigation interventions. This work aims to conduct a feasibility study for accessing the effect of multiple interventions to control the COVID-19 breakouts in the UK and other European countries, accounting for balance of healthcare demand. The model is to infer the impact of mitigation, suppression and multiple rolling interventions for controlling COVID-19 outbreaks in the UK, with two features considered: direct link between exposed and recovered population, and practical healthcare demand by separation of infections. We combined the calibrated model with COVID-19 data in London and non-London regions in the UK during February and April 2020. Our finding suggests that rolling intervention is an optimal strategy to effectively control COVID-19 outbreaks in the UK for balancing healthcare demand and morality ratio. It is better to implement regional based interventions with varied intensities and maintenance periods. We suggest an intervention strategy named as "Besieged and rolling interventions" to the UK that take a consistent suppression in London for 100 days and 3 weeks rolling intervention in other regions. This strategy would reduce the overall infections and deaths of COVID-19 outbreaks, and balance healthcare demand in the UK.

Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan.

Recent outbreaks of coronavirus disease 2019 (COVID-19) has led a global pandemic cross the world. Most countries took two main interventions: suppression like immediate lockdown cities at epicenter or mitigation that slows down but not stopping epidemic for reducing peak healthcare demand. Both strategies have their apparent merits and limitations; it becomes extremely hard to conduct one intervention as the most feasible way to all countries. Targeting at this problem, this paper conducted a feasibility study by defining a mathematical model named SEMCR, it extended traditional SEIR (Susceptible-Exposed-Infectious-Recovered) model by adding two key features: a direct connection between Exposed and Recovered populations, and separating infections into mild and critical cases. It defined parameters to classify two stages of COVID-19 control: active contain by isolation of cases and contacts, passive contain by suppression or mitigation. The model was fitted and evaluated with public dataset containing daily number of confirmed active cases including Wuhan and London during January 2020 and March 2020. The simulated results showed that 1) Immediate suppression taken in Wuhan significantly reduced the total exposed and infectious populations, but it has to be consistently maintained at least 90 days (by the middle of April 2020). Without taking this intervention, we predict the number of infections would have been 73 folders higher by the middle of April 2020. Its success requires efficient government initiatives and effective collaborative governance for mobilizing of corporate resources to provide essential goods. This mode may be not suitable to other countries without efficient collaborative governance and sufficient health resources. 2) In London, it is possible to take a hybrid intervention of suppression and mitigation for every 2 or 3 weeks over a longer period to balance the total infections and economic loss. While the total infectious populations in this scenario would be possibly 2 times than the one taking suppression, economic loss and recovery of London would be less affected. 3) Both in Wuhan and London cases, one important issue of fitting practical data was that there were a portion (probably 62.9% in Wuhan) of self-recovered populations that were asymptomatic or mild symptomatic. This finding has been recently confirmed by other studies that the seroprevalence in Wuhan varied between 3.2% and 3.8% in different sub-regions. It highlights that the epidemic is far from coming to an end by means of herd immunity. Early release of intervention intensity potentially increased a risk of the second outbreak.