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We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments. Under consideration for publication in Theory and Practice of Logic Programming (TPLP)., Comment: Under consideration for publication in Theory and Practice of Logic Programming (TPLP)

International audience; Safe and effective vaccines against coronavirus disease 2019 (COVID-19) are essential for ending the ongoing pandemic. Although impressive progress has been made with several COVID-19 vaccines already approved, it is clear that those developed so far cannot meet the global vaccine demand alone. We describe a COVID-19 vaccine based on a replication-defective gorilla adenovirus expressing the stabilized prefusion severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein named GRAd-COV2. We assessed the safety and immunogenicity of a single-dose regimen of this vaccine in healthy younger and older adults to select the appropriate dose for each age group. For this purpose, a phase 1, dose-escalation, open-labeled trial was conducted including 90 healthy participants (45 aged 18 to 55 years old and 45 aged 65 to 85 years old) who received a single intramuscular administration of GRAd-COV2 at three escalating doses. Local and systemic adverse reactions were mostly mild or moderate and of short duration, and no serious adverse events were reported. Four weeks after vaccination, seroconversion to spike protein and receptor binding domain was achieved in 43 of 44 young volunteers and in 45 of 45 older participants. Consistently, neutralizing antibodies were detected in 42 of 44 younger-age and 45 of 45 older-age volunteers. In addition, GRAd-COV2 induced a robust and T helper 1 cell (T H 1)-skewed T cell response against the spike protein in 89 of 90 participants from both age groups. Overall, the safety and immunogenicity data from the phase 1 trial support the further development of this vaccine.

Safe and effective vaccines against coronavirus disease 2019 (COVID-19) are urgently needed to control the ongoing pandemic. Although impressive progress has been made with several COVID-19 vaccines already approved, it is clear that those developed so far cannot meet the global vaccine demand. We have developed a COVID-19 vaccine based on a replication-defective gorilla adenovirus expressing the stabilized pre-fusion SARS-CoV-2 Spike protein, named GRAd-COV2. We aimed to assess the safety and immunogenicity of a single-dose regimen of this vaccine in healthy younger and older adults to select the appropriate dose for each age group. To this purpose, a phase 1, dose-escalation, open-label trial was conducted including 90 healthy subjects, (45 aged 18-55 years and 45 aged 65-85 years), who received a single intramuscular administration of GRAd-CoV2 at three escalating doses. Local and systemic adverse reactions were mostly mild or moderate and of short duration, and no serious AE was reported. Four weeks after vaccination, seroconversion to Spike/RBD was achieved in 43/44 young volunteers and in 45/45 older subjects. Consistently, neutralizing antibodies were detected in 42/44 younger age and 45/45 older age volunteers. In addition, GRAd-COV2 induced a robust and Th1-skewed T cell response against the S antigen in 89/90 subjects from both age groups. Overall, the safety and immunogenicity data from the phase 1 trial support further development of this vaccine.One Sentence SummaryGRAd-COV2, a candidate vaccine for COVID-19 based on a novel gorilla adenovirus, is safe and immunogenic in younger and older adults

The COVID-19 pandemic caused by the emergent SARS-CoV-2 coronavirus threatens global public health and there is an urgent need to develop safe and effective vaccines. Here we report the generation and the preclinical evaluation of a novel replication-defective gorilla adenovirus-vectored vaccine encoding the pre-fusion stabilized Spike (S) protein of SARS-CoV2. We show that our vaccine candidate, GRAd-COV2, is highly immunogenic both in mice and macaques, eliciting both functional antibodies which neutralize SARS-CoV-2 infection and block Spike protein binding to the ACE2 receptor, and a robust, Th1-dominated cellular response. We show here that the pre-fusion stabilized Spike antigen is superior to the wild type in inducing ACE2-interfering, SARS-CoV2 neutralizing antibodies. To face the unprecedented need for vaccine manufacturing at massive scale, different GRAd genome deletions were compared to select the vector backbone showing the highest productivity in stirred tank bioreactors. This preliminary dataset identified GRAd-COV2 as a potential COVID-19 vaccine candidate, supporting the translation of GRAd-COV2 vaccine in a currently ongoing Phase I clinical trial (NCT04528641)., Graphical Abstract, Safe and effective vaccines are needed to fight COVID-19 pandemic. Here we report the preclinical evaluation of GRAd-COV2, a replication-defective adenovirus vector derived from a gorilla and encoding the pre-fusion stabilized Spike protein of SARS-CoV2. GRAd-COV2 is currently being tested in Phase 2/3 clinical studies.

Socially Assistive Robots used for elderly care are required to determine the location of a person and to approach him/her in order to provide assistance. Human tracking systems are applied to detect and track people that are already in the proximity of the robot, while its limited field of view makes the user easily lost. Moreover, navigation algorithms typically need the availability of reliable sensors on the robot and the possibility of marking possible user locations. On the contrary, in this work, we investigate the opportunity to use a reactive control mechanism for detecting and approaching people. Our approach is tested on two commercial mobile robots that present a different sensors configuration and by using off-the-shelf algorithms for people localization and tracking. Results show the feasibility of the approach with respect to the considered domain that does not require precise positioning, but hopes for a real application of such low-cost robot into the wild. Features of the considered robots and their impact on performance are also discussed.

Social Assistive Robots are mainly designed for tasks requiring the interaction with the user. However, they could also be involved in other non-interactive tasks which execution may potentially distract the user from his/her current activity. In this direction, we aim at evaluating the disengagement levels caused by a robot's movements in the human's surroundings. In particular, we consider a robotic system approaching an elder person to monitor his/her behavior, while he/she is occupied in carrying out a specific daily activity. We conducted a classic video analysis of human behaviors in order to identify relevant non-verbal disengagement signals such as the human gaze and pose variation when he/she is approached by the robot. Results obtained from ambient cameras are compared with the ones from the robot camera showing a moderate correlation between them. Additionally, the role of other contextual factors, such as the activity posture, approaching distance, and cognitive load are discussed in the direction of an automatic evaluation of the user's distraction to be used by the robot to plan its motion.

Assistive technology is playing a crucial role in supporting caregivers and patients with neurological disorders to carry out home care services. Nevertheless, the technological advances allowing for the automation of such services often lead to systems composed of several devices that make patients not always comfortable when interacting with them, so limiting their effective use. In order to improve the acceptance level of an assistive system, a robotic system is proposed to act as a smart sensor whose functionalities to control its behavior as well as to provide meaningful observations from the input data are modeled as services. The system is supported by a middleware able to automatically schedule a set of home care services that are personalized for each patient considering the personal daily routine, the cognitive status, and the personality profile. In addition, the proposed system is able to react to dynamic changes in the patient's state by modifying the robot behavior and adapting on the fly the set of proposed home care services.