Improving Social and Personal Rhythm Dysregulation in Young and Old Adults with Bipolar Disorder: Post-Hoc Analysis of a Feasibility Randomized Controlled Trial Using Virtual Reality-Based Intervention.

Introduction: Rehabilitative interventions employing technology play a crucial role in bipolar disorder (BD) treatment. The study aims to appraise the virtual reality (VR)-based cognitive remediation (CR) and the interpersonal rhythm approaches to treatment outcomes of BD across different age groups. Methods: Post-hoc analysis of a 12-week randomizedcontrolled cross-over feasibility trial involving people with mood disorders (BD, DSM-IV) aged 18-75 years old: thirty-nine exposed to the experimental VR-based CR vs 25 waiting list controls. People with BD relapse, epilepsy or severe eye diseases (due to the potential VR risks exposure) were excluded. Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) was used to measure the outcome. Results: Cases and controls did not statistically significantly differ in age and sex distributions. Personal rhythm scores improved over the study follow-up in the experimental vs the control group (APC = 8.7%; F = 111.9; p < 0.0001), both in young (18-45 years) (APC = 5.5%; F = 70.46; p < 0.0001) and, to a lesser extent, older (>46 years) adults (APC = 10.5%; F = 12.110; p = 0.002). Conclusions: This study observed improved synchronization of personal and social rhythms in individuals with BD after a virtual reality cognitive remediation intervention, particularly in social activity, daily activities, and chronotype, with greater benefits in the younger population.

Optimal configurations for stiffness and compliance in human & robot arms.

Research in neurophysiology has shown that humans are able to adapt the mechanical stiffness at the hand in order to resist disturbances. This has served as inspiration for optimising stiffness in robot arms during manipulation tasks. Endpoint stiffness is modelled in Cartesian space, as though the hand were in independent rigid body. But an arm is a series of rigid bodies connected by articulated joints. The contribution of the joints and arm configuration to the endpoint stiffness has not yet been quantified. In this paper we use mathematical optimisation to find conditions for maximum stiffness and compliance with respect to an externally applied force. By doing so, we can retroactively explain observations made about humans using these mathematically optimal conditions. We then show how this optimisation can be applied to robotic task planning and control. Experiments on a humanoid robot show similar arm posture to that observed in humans. This suggests there is an underlying physical principle by which humans optimise stiffness. We can use this to derive natural control methods for robots.

In-domain versus out-of-domain transfer learning in plankton image classification.

Plankton microorganisms play a huge role in the aquatic food web. Recently, it has been proposed to use plankton as a biosensor, since they can react to even minimal perturbations of the aquatic environment with specific physiological changes, which may lead to alterations in morphology and behavior. Nowadays, the development of high-resolution in-situ automatic acquisition systems allows the research community to obtain a large amount of plankton image data. Fundamental examples are the ZooScan and Woods Hole Oceanographic Institution (WHOI) datasets, comprising up to millions of plankton images. However, obtaining unbiased annotations is expensive both in terms of time and resources, and in-situ acquired datasets generally suffer from severe imbalance, with only a few images available for several species. Transfer learning is a popular solution to these challenges, with ImageNet1K being the most-used source dataset for pre-training. On the other hand, datasets like the ZooScan and the WHOI may represent a valuable opportunity to compare out-of-domain and large-scale plankton in-domain source datasets, in terms of performance for the task at hand.In this paper, we design three transfer learning pipelines for plankton image classification, with the aim of comparing in-domain and out-of-domain transfer learning on three popular benchmark plankton datasets. The general framework consists in fine-tuning a pre-trained model on a plankton target dataset. In the first pipeline, the model is pre-trained from scratch on a large-scale plankton dataset, in the second, it is pre-trained on large-scale natural image datasets (ImageNet1K or ImageNet22K), while in the third, a two-stage fine-tuning is implemented (ImageNet [Formula: see text] large-scale plankton dataset [Formula: see text] target plankton dataset). Our results show that an out-of-domain ImageNet22K pre-training outperforms the plankton in-domain ones, with an average boost in test accuracy of around 6%. In the next part of this work, we adopt three ImageNet22k pre-trained Vision Transformers and one ConvNeXt, obtaining results on par (or slightly superior) with the state-of-the-art, corresponding to the usage of CNN models ensembles, with a single model. Finally, we design and test an ensemble of our Vision Transformers and the ConvNeXt, outperforming the state-of-the-art existing works on plankton image classification on the three target datasets. To support scientific community contribution and further research, our implemented code is open-source and available at https://github.com/Malga-Vision/plankton_transfer .

The impact of facial expression and communicative gaze of a humanoid robot on individual Sense of Agency.

Sense of Agency (SoA) is the feeling of control over one's actions and their outcomes. A well-established implicit measure of SoA is the temporal interval estimation paradigm, in which participants estimate the time interval between a voluntary action and its sensory consequence. In the present study, we aimed to investigate whether the valence of action outcome modulated implicit SoA. The valence was manipulated through interaction partner's (i) positive/negative facial expression, or (ii) type of gaze (gaze contact or averted gaze). The interaction partner was the humanoid robot iCub. In Experiment 1, participants estimated the time interval between the onset of their action (head movement towards the robot), and the robot's facial expression (happy vs. sad face). Experiment 2 was identical, but the outcome of participants' action was the type of robot's gaze (gaze contact vs. averted). In Experiment 3, we assessed-in a within-subject design-the combined effect of robot's type of facial expression and type of gaze. Results showed that, while the robot's facial expression did not affect participants' SoA (Experiment 1), the type of gaze affected SoA in both Experiment 2 and Experiment 3. Overall, our findings showed that the robot's gaze is a more potent factor than facial expression in modulating participants' implicit SoA.

A Recovery-Oriented Program for People with Bipolar Disorder through Virtual Reality-Based Cognitive Remediation: Results of a Feasibility Randomized Clinical Trial.

BACKGROUND: Cognitive impairment is a frequent consequence of bipolar disorder (BD) that is difficult to prevent and treat. In addition, the quality of the preliminary evidence on the treatment of BD through Cognitive Remediation (CR) with traditional methods is poor. This study aims to evaluate the feasibility of a CR intervention with fully immersive Virtual Reality (VR) as an additional treatment for BD and offers preliminary data on its efficacy. METHODS: Feasibility randomized controlled cross-over clinical study, with experimental condition lasting three months, crossed between two groups. Experimental condition: CR fully immersive VR recovery-oriented program plus conventional care; Control condition: conventional care. The control group began the experimental condition after a three months period of conventional care (waiting list). After the randomization of 50 people with BD diagnosis, the final sample consists of 39 participants in the experimental condition and 25 in the control condition because of dropouts. RESULTS: Acceptability and tolerability of the intervention were good. Compared to the waitlist group, the experimental group reported a significant improvement regarding cognitive functions (memory: p = 0.003; attention: p = 0.002, verbal fluency: p = 0.010, executive function: p = 0.003), depressive symptoms (p = 0.030), emotional awareness (p = 0.007) and biological rhythms (p = 0.029). CONCLUSIONS: The results are preliminary and cannot be considered exhaustive due to the small sample size. However, the evidence of efficacy, together with the good acceptability of the intervention, is of interest. These results suggest the need to conduct studies with larger samples that can confirm this data. TRIAL REGISTRATION: ClinicalTrialsgov NCT05070065, registered in September 2021.

Fully Immersive Virtual Reality-Based Cognitive Remediation for Adults with Psychosocial Disabilities: A Systematic Scoping Review of Methods Intervention Gaps and Meta-Analysis of Published Effectiveness Studies.

BACKGROUND: Cognitive Remediation (CR) programs are effective for the treatment of mental diseases; in recent years, Virtual Reality (VR) rehabilitation tools are increasingly used. This study aimed to systematically review and meta-analyze the published randomized controlled trials that used fully immersive VR tools for CR programs in psychiatric rehabilitation. We also wanted to map currently published CR/VR interventions, their methods components, and their evidence base, including the framework of the development intervention of CR in fully immersive VR. METHODS: Level 1 of evidence. This study followed the PRISMA extension for Scoping Reviews and Systematic Review. Three electronic databases (Pubmed, Cochrane Library, Embase) were systematically searched, and studies were included if they met the eligibility criteria: only randomized clinical trials, only studies with fully immersive VR, and only CR for the adult population with mental disorders. RESULTS: We found 4905 (database) plus 7 (manual/citation searching articles) eligible studies. According to inclusion criteria, 11 studies were finally reviewed. Of these, nine included patients with mild cognitive impairment, one with schizophrenia, and one with mild dementia. Most studies used an ecological scenario, with improvement across all cognitive domains. Although eight studies showed significant efficacy of CR/VR, the interventions' development was poorly described, and few details were given on the interventions' components. CONCLUSIONS: Although CR/VR seems to be effective in clinical and feasibility outcomes, the interventions and their components are not clearly described. This limits the understanding of the effectiveness and undermines their real-world implementation and the establishment of a gold standard for fully immersive VR/CR.

Tour guide robot: a 5G-enabled robot museum guide.

This paper presents and discusses the development and deployment of a tour guide robot as part of the 5 g-TOURS EU research project, aimed at developing applications enabled by 5G technology in different use cases. The objective is the development of an autonomous robotic application where intelligence is off-loaded to a remote machine via 5G network, so as to lift most of the computational load from the robot itself. The application uses components that have been widely studied in robotics, (i.e., localization, mapping, planning, interaction). However, the characteristics of the network and interactions with visitors in the wild introduce specific problems which must be taken into account. The paper discusses in detail such problems, summarizing the main results achieved both from the methodological and the experimental standpoint, and is completed by the description of the general functional architecture of the whole system, including navigation and operational services. The software implementation is also publicly available.

Assessing the Impact of Conversational Artificial Intelligence in the Treatment of Stress and Anxiety in Aging Adults: Randomized Controlled Trial.

BACKGROUND: While mental health applications are increasingly becoming available for large populations of users, there is a lack of controlled trials on the impacts of such applications. Artificial intelligence (AI)-empowered agents have been evaluated when assisting adults with cognitive impairments; however, few applications are available for aging adults who are still actively working. These adults often have high stress levels related to changes in their work places, and related symptoms eventually affect their quality of life. OBJECTIVE: We aimed to evaluate the contribution of TEO (Therapy Empowerment Opportunity), a mobile personal health care agent with conversational AI. TEO promotes mental health and well-being by engaging patients in conversations to recollect the details of events that increased their anxiety and by providing therapeutic exercises and suggestions. METHODS: The study was based on a protocolized intervention for stress and anxiety management. Participants with stress symptoms and mild-to-moderate anxiety received an 8-week cognitive behavioral therapy (CBT) intervention delivered remotely. A group of participants also interacted with the agent TEO. The participants were active workers aged over 55 years. The experimental groups were as follows: group 1, traditional therapy; group 2, traditional therapy and mobile health (mHealth) agent; group 3, mHealth agent; and group 4, no treatment (assigned to a waiting list). Symptoms related to stress (anxiety, physical disease, and depression) were assessed prior to treatment (T1), at the end (T2), and 3 months after treatment (T3), using standardized psychological questionnaires. Moreover, the Patient Health Questionnaire-8 and General Anxiety Disorders-7 scales were administered before the intervention (T1), at mid-term (T2), at the end of the intervention (T3), and after 3 months (T4). At the end of the intervention, participants in groups 1, 2, and 3 filled in a satisfaction questionnaire. RESULTS: Despite randomization, statistically significant differences between groups were present at T1. Group 4 showed lower levels of anxiety and depression compared with group 1, and lower levels of stress compared with group 2. Comparisons between groups at T2 and T3 did not show significant differences in outcomes. Analyses conducted within groups showed significant differences between times in group 2, with greater improvements in the levels of stress and scores related to overall well-being. A general worsening trend between T2 and T3 was detected in all groups, with a significant increase in stress levels in group 2. Group 2 reported higher levels of perceived usefulness and satisfaction. CONCLUSIONS: No statistically significant differences could be observed between participants who used the mHealth app alone or within the traditional CBT setting. However, the results indicated significant differences within the groups that received treatment and a stable tendency toward improvement, which was limited to individual perceptions of stress-related symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT04809090; https://clinicaltrials.gov/ct2/show/NCT04809090.

Toward an Attentive Robotic Architecture: Learning-Based Mutual Gaze Estimation in Human-Robot Interaction.

Social robotics is an emerging field that is expected to grow rapidly in the near future. In fact, it is increasingly more frequent to have robots that operate in close proximity with humans or even collaborate with them in joint tasks. In this context, the investigation of how to endow a humanoid robot with social behavioral skills typical of human-human interactions is still an open problem. Among the countless social cues needed to establish a natural social attunement, this article reports our research toward the implementation of a mechanism for estimating the gaze direction, focusing in particular on mutual gaze as a fundamental social cue in face-to-face interactions. We propose a learning-based framework to automatically detect eye contact events in online interactions with human partners. The proposed solution achieved high performance both in silico and in experimental scenarios. Our work is expected to be the first step toward an attentive architecture able to endorse scenarios in which the robots are perceived as social partners.

HR1 Robot: An Assistant for Healthcare Applications.

According to the World Health Organization the percentage of healthcare dependent population, such as elderly and people with disabilities, among others, will increase over the next years. This trend will put a strain on the health and social systems of most countries. The adoption of robots could assist these health systems in responding to this increased demand, particularly in high intensity and repetitive tasks. In a previous work, we compared a Socially Assistive Robot (SAR) with a Virtual Agent (VA) during the execution of a rehabilitation task. The SAR consisted of a humanoid R1 robot, while the Virtual Agent represented its simulated counter-part. In both cases, the agents evaluated the participants' motions and provided verbal feedback. Participants reported higher levels of engagement when training with the SAR. Given that the architecture has been proven to be successful for a rehabilitation task, other sets of repetitive tasks could also take advantage of the platform, such as clinical tests. A commonly performed clinical trial is the Timed Up and Go (TUG), where the patient has to stand up, walk 3 m to a goal line and back, and sit down. To handle this test, we extended the architecture to evaluate lower limbs' motions, follow the participants while continuously interacting with them, and verify that the test is completed successfully. We implemented the scenario in Gazebo, by simulating both participants and the interaction with the robot. A full interactive report is created when the test is over, providing the extracted information to the specialist. We validate the architecture in three different experiments, each with 1,000 trials, using the Gazebo simulation. These experiments evaluate the ability of this architecture to analyse the patient, verify if they are able to complete the TUG test, and the accuracy of the measurements obtained during the test. This work provides the foundations towards more thorough clinical experiments with a large number of participants with a physical platform in the future. The software is publicly available in the assistive-rehab repository and fully documented.

Cognitive Remediation Virtual Reality Tool a Recovery-Oriented Project for People with Bipolar Disorder: Protocol of a Feasibility Randomized Clinical Trial.

Introduction: Cognitive deficits are considered a fundamental component of bipolar disorder due to the fact that they negatively impact personal/social functioning. Cognitive remediation interventions are effective in the treatment of various psychosocial disorders, including bipolar disorder. The use of Virtual reality as a rehabilitation tool has produced scientific evidence in recent years, especially in cardiovascular, neurological, and musculoskeletal rehabilitation. This study aims at evaluating the feasibility of a Cognitive Remediation Virtual Reality Program (CEREBRUM) for people with bipolar disorder in psychiatric rehabilitation. Material and Methods: Feasibility randomized controlled cross-over clinical study; we randomized 50 people from the Consultation and Psychosomatic Psychiatry Center of the University Hospital of Cagliari (San Giovanni di Dio Civil Hospital) with a diagnosis of bipolar disorder. We propose a cognitive remediation program in virtual reality (CEREBRUM), 3 months with 2 weekly sessions, for the experimental group and a usual care program for the control group (psychiatric visit and/or psychotherapy). Results: The results of the trial will be published in international peer-reviewed journals and will be disseminated at international meetings and congress. Discussion: This RCT aims, with regards to its feasibility and design, to provide information about a confirmatory trial that evaluates the effectiveness of a Virtual Reality Cognitive Remediation program in psychiatric rehabilitation for the treatment of cognitive dysfunction in people with bipolar disorder. Conclusion: The results that we analyzed at the end of the RCT will have an impact on psychiatric rehabilitation research with a focus on improving the application of technologies for mental health.Trial registration: ClinicalTrialsgov NCT05070065, registered on September 2021.

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation.

Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.

A Differentiable Extended Kalman Filter for Object Tracking Under Sliding Regime.

Tactile sensing represents a valuable source of information in robotics for perception of the state of objects and their properties. Modern soft tactile sensors allow perceiving orthogonal forces and, in some cases, relative motions along the surface of the object. Detecting and measuring this kind of lateral motion is fundamental to react to possibly uncontrolled slipping and sliding of the object being manipulated. Object slip detection and prediction have been extensively studied in the robotic community leading to solutions with good accuracy and suitable for closed-loop grip stabilization. However, algorithms for object perception, such as in-hand object pose estimation and tracking algorithms, often assume no relative motion between the object and the hand and rarely consider the problem of tracking the pose of the object subjected to slipping and sliding motions. In this work, we propose a differentiable Extended Kalman filter that can be trained to track the position and the velocity of an object under translational sliding regime from tactile observations alone. Experiments with several objects, carried out on the iCub humanoid robot platform, show that the proposed approach allows achieving an average position tracking error in the order of 0.6 cm, and that the provided estimate of the object state can be used to take control decisions using tactile feedback alone. A video of the experiments is available as Supplementary Material.

MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking.

Tracking the 6D pose and velocity of objects represents a fundamental requirement for modern robotics manipulation tasks. This paper proposes a 6D object pose tracking algorithm, called MaskUKF, that combines deep object segmentation networks and depth information with a serial Unscented Kalman Filter to track the pose and the velocity of an object in real-time. MaskUKF achieves and in most cases surpasses state-of-the-art performance on the YCB-Video pose estimation benchmark without the need for expensive ground truth pose annotations at training time. Closed loop control experiments on the iCub humanoid platform in simulation show that joint pose and velocity tracking helps achieving higher precision and reliability than with one-shot deep pose estimation networks. A video of the experiments is available as Supplementary Material.

The Evolution of Covid-19 in Italy after the Spring of 2020: An Unpredicted Summer Respite Followed by a Second Wave.

The coronavirus (COVID-19) pandemic was particularly invasive in Italy during the period between March and late April 2020, then decreased in both the number of infections and in the seriousness of the illness throughout the summer of 2020. In this work, we measure the severity of the disease by the ratio of Intensive Care Units (ICU) spaces occupied by COVID-19 patients and the number of Active Cases (AC) each month from April to October 2020. We also use the ratio of the number of Deaths (D) to the number of Active Cases. What clearly emerges, from rigorous statistical analysis, is a progressive decrease in both ratios until August, indicating progressive mitigation of the disease. This is particularly evident when comparing March-April with July-August; during the summer period the two ratios became roughly 18 times lower. We test such sharp decreases against possible bias in counting active cases and we confirm their statistical significance. We then interpret such evidence in terms of the well-known seasonality of the human immune system and the virus-inactivating effect of stronger UV rays in the summer. Both ratios, however, increased again in October, as ICU/AC began to increase in September 2020. These ratios and the exponential growth of infections in October indicate that the virus-if not contained by strict measures-will lead to unsustainable challenges for the Italian health system in the winter of 2020-2021.

Carbon Nanofiber versus Graphene-Based Stretchable Capacitive Touch Sensors for Artificial Electronic Skin.

Stretchable capacitive devices are instrumental for new-generation multifunctional haptic technologies particularly suited for soft robotics and electronic skin applications. A majority of elongating soft electronics still rely on silicone for building devices or sensors by multiple-step replication. In this study, fabrication of a reliable elongating parallel-plate capacitive touch sensor, using nitrile rubber gloves as templates, is demonstrated. Spray coating both sides of a rubber piece cut out of a glove with a conductive polymer suspension carrying dispersed carbon nanofibers (CnFs) or graphene nanoplatelets (GnPs) is sufficient for making electrodes with low sheet resistance values (≈10 Ω sq-1). The electrodes based on CnFs maintain their conductivity up to 100% elongation whereas the GnPs-based ones form cracks before 60% elongation. However, both electrodes are reliable under elongation levels associated with human joints motility (≈20%). Strikingly, structural damages due to repeated elongation/recovery cycles could be healed through annealing. Haptic sensing characteristics of a stretchable capacitive device by wrapping it around the fingertip of a robotic hand (ICub) are demonstrated. Tactile forces as low as 0.03 N and as high as 5 N can be easily sensed by the device under elongation or over curvilinear surfaces.

YARP-ROS Inter-Operation in a 2D Navigation Task.

This paper presents some recent developments in YARP middleware, aimed to improve its integration with ROS. They include a new mechanism to read/write ROS transform frames and a new set of standard interfaces to intercommunicate with the ROS navigation stack. A novel set of YARP companion modules, which provide basic navigation functionalities for robots unable to run ROS, is also presented. These modules are optional, independent from each other, and they provide compatible functionalities to well-known packages available inside ROS framework. This paper also discusses how developers can customize their own hybrid YARP-ROS environment in the way it best suits their needs (e.g., the system can be configured to have a YARP application sending navigation commands to a ROS path planner, or vice versa). A number of available possibilities is presented through a set of chosen test cases applied to both real and simulated robots. Finally, example applications discussed in this paper are also made available to the community by providing snippets of code and links to source files hosted on github repository https://github.com/robotology.

A Framework for Fast, Autonomous, and Reliable Tool Incorporation on iCub.

One of the main advantages of building robots with size and motor capabilities close to those of humans, such as iCub, lies in the fact that they can potentially take advantage of a world populated with tools and devices designed by and for humans. However, in order to be able to do proper use of the tools around them, robots need to be able to incorporate these tools, that is, to build a representation of the tool's geometry, reach and pose with respect to the robot. The present paper tackles this argument by presenting a repository which implements a series of interconnected methods that enable autonomous, fast, and reliable tool incorporation on the iCub platform.

Progress in Neuroengineering for brain repair: New challenges and open issues.

BACKGROUND: In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome. RESULTS: In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise. CONCLUSIONS: Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

iCub: The not-yet-finished story of building a robot child.

The iCub open-source humanoid robot child is a successful initiative supporting research in embodied artificial intelligence.