This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. • IEEE ROBOTICS & AUTOMATION MAGAZINE • MONTH 2021© IEEE 2021. This article is free to access and download, along with rights for full text and data mining, re-use and analysis.T he COVID-19 pandemic and the related emergency have contributed to the push for innovative solutions applied to health care. In particular, robotics has shown huge potential for contributing to pandemic relief efforts and improving people's quality of life in several scenarios. In this article, a robotic system, characterized by interaction capabilities and autonomous navigation, is developed to be used in a COVID-19 health-care treatment center for logistics and disinfection purposes. The article describes the two-month use of the platform in the University Hospital Campus Bio-Medico (UCBM) COVID-19 treatment center in Rome, Italy, and presents experimental results for the robot's navigation capabilities in unstructured environments and collaborative activities with health-care operators in the clinical setting. Managing a PandemicOn 31 December 2019, a pneumonia of unknown cause was detected in Wuhan, China. In a short time, the outbreak was declared a public health emergency of international concern. On 11 February 2020, the World Health Organization announced the name of the new disease: COVID-19. Severe acute respiratory syndrome coronavirus has affected people worldwide, causing more than 440,000 deaths [1].During the past several months, various solutions from the fields of prevention, diagnosis, and treatment have been considered for managing the pandemic. Although no specific treatment has been developed, the main solutions seem to be preventive measurements, i.e., quarantines, social distancing, and hygienic precautions [1]. The latter refers to both personal hygiene and surface disinfection, which are pivotally important to minimize the risk of contamination, especially in public environments, such as hospitals. Moreover, a fast diagnosis is crucial to limit the diffusion of the virus, especially by asymptomatic people, and the real-time polymerase chain reaction (RT-PCR) plays a fundamental role in the early identification of affected subjects. For this reason, to date,

No abstract

Despite being able to derive quantitative data on motor performance, rehabilitation robots typically do not provide information on users' subjective experience, and in particular on their psychophysiological state. The aim of this work was the development of a method for the psychophysiological assessment of users whose treadmill walking is assisted by a lower limb exoskeleton. Four indicators were estimated based on physiological data by using a Fuzzy logic approach. The assessment of human-exoskeleton interaction was performed on a group of four healthy participants and the correlation between different pairs of indicators was evaluated.

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD—Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker’s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.