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BACKGROUND: Social robots are becoming increasingly important as companions in our daily lives. Consequently, humans expect to interact with them using the same mental models applied to human-human interactions, including the use of cospeech gestures. Research efforts have been devoted to understanding users' needs and developing robot's behavioral models that can perceive the user state and properly plan a reaction. Despite the efforts made, some challenges regarding the effect of robot embodiment and behavior in the perception of emotions remain open. OBJECTIVE: The aim of this study is dual. First, it aims to assess the role of the robot's cospeech gestures and embodiment in the user's perceived emotions in terms of valence (stimulus pleasantness), arousal (intensity of evoked emotion), and dominance (degree of control exerted by the stimulus). Second, it aims to evaluate the robot's accuracy in identifying positive, negative, and neutral emotions displayed by interacting humans using 3 supervised machine learning algorithms: support vector machine, random forest, and K-nearest neighbor. METHODS: Pepper robot was used to elicit the 3 emotions in humans using a set of 60 images retrieved from a standardized database. In particular, 2 experimental conditions for emotion elicitation were performed with Pepper robot: with a static behavior or with a robot that expresses coherent (COH) cospeech behavior. Furthermore, to evaluate the role of the robot embodiment, the third elicitation was performed by asking the participant to interact with a PC, where a graphical interface showed the same images. Each participant was requested to undergo only 1 of the 3 experimental conditions. RESULTS: A total of 60 participants were recruited for this study, 20 for each experimental condition for a total of 3600 interactions. The results showed significant differences (P<.05) in valence, arousal, and dominance when stimulated with the Pepper robot behaving COH with respect to the PC condition, thus underlying the importance of the robot's nonverbal communication and embodiment. A higher valence score was obtained for the elicitation of the robot (COH and robot with static behavior) with respect to the PC. For emotion recognition, the K-nearest neighbor classifiers achieved the best accuracy results. In particular, the COH modality achieved the highest level of accuracy (0.97) when compared with the static behavior and PC elicitations (0.88 and 0.94, respectively). CONCLUSIONS: The results suggest that the use of multimodal communication channels, such as cospeech and visual channels, as in the COH modality, may improve the recognition accuracy of the user's emotional state and can reinforce the perceived emotion. Future studies should investigate the effect of age, culture, and cognitive profile on the emotion perception and recognition going beyond the limitation of this work.

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Socially Assistive Robots (SARs) are designed to support us in our daily life as a companion, and assistance but also to support the caregivers' work. SARs should show personalized and human-like behavior to improve their acceptance and, consequently, their use. Additionally, they should be trustworthy by caregivers and professionals to be used as support for their work (e.g. objective assessment, decision support tools). In this context the aim of the paper is dual. Firstly, this paper aims to present and discuss the robot behavioral model based on sensing, perception, decision support, and interaction modules. The novel idea behind the proposed model is to extract and use the same multimodal features set for two purposes: (i) to profile the user, so to be used by the caregiver as a decision support tool for the assessment and monitoring of the patient; (ii) to fine-tune the human-robot interaction if they can be correlated to the social cues. Secondly, this paper aims to test in a real environment the proposed model using a SAR robot, namely ASTRO. Particularly, it measures the body posture, the gait cycle, and the handgrip strength during the walking support task. Those collected data were analyzed to assess the clinical profile and to fine-tune the physical interaction. Ten older people (65.2 ± 15.6 years) were enrolled for this study and were asked to walk with ASTRO at their normal speed for 10 m. The obtained results underline a good estimation (p < 0.05) of gait parameters, handgrip strength, and angular excursion of the torso with respect to most used instruments. Additionally, the sensory outputs were combined in the perceptual model to profile the user using non-classical and unsupervised techniques for dimensionality reduction namely T-distributed Stochastic Neighbor Embedding (t-SNE) and non-classic multidimensional scaling (nMDS). Indeed, these methods can group the participants according to their residual walking abilities.

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Cognitive disability strongly reduces people's autonomy in performing desired as well as daily activities. The use of Social Assistive Robots (SARs) for cognitive rehabilitation therapy for disabled people could be a valuable gateway for the residential facility of the future. In this work, we design and develop a SAR that can be used for cognitive therapy proposing music and game activities. The results confirm that participants were positively engaged during the proposed activities and satisfied by the robot, despite the low perception of its usability. Professional caregivers noticed and confirmed the high level of engagement and the positive acceptance of the robot within the session, suggesting future tasks for SAR. Clinical Relevance- The results suggest the potential use of SAR also with disabled people proposing cognitive games as a part of the cognitive rehabilitation program.

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By 2030, the World Health Organization (WHO) foresees a worldwide workforce shortfall of healthcare professionals, with dramatic consequences for patients, economies, and communities. Research in assistive robotics has experienced an increasing attention during the last decade demonstrating its utility in the realization of intelligent robotic solutions for healthcare and social assistance, also to compensate for such workforce shortages. Nevertheless, a challenge for effective assistive robots is dealing with a high variety of situations and contextualizing their interactions according to living contexts and habits (or preferences) of assisted people. This study presents a novel cognitive system for assistive robots that rely on artificial intelligence (AI) representation and reasoning features/services to support decision-making processes of healthcare assistants. We proposed an original integration of AI-based features, that is, knowledge representation and reasoning and automated planning to 1) define a human-in-the-loop continuous assistance procedure that helps clinicians in evaluating and managing patients and; 2) to dynamically adapt robot behaviors to the specific needs and interaction abilities of patients. The system is deployed in a realistic assistive scenario to demonstrate its feasibility to support a clinician taking care of several patients with different conditions and needs.

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This paper makes the VISTA database, composed of inertial and visual data, publicly available for gesture and activity recognition. The inertial data were acquired with the SensHand, which can capture the movement of wrist, thumb, index and middle fingers, while the RGB-D visual data were acquired simultaneously from two different points of view, front and side. The VISTA database was acquired in two experimental phases: in the former, the participants have been asked to perform 10 different actions; in the latter, they had to execute five scenes of daily living, which corresponded to a combination of the actions of the selected actions. In both phase, Pepper interacted with participants. The two camera point of views mimic the different point of view of pepper. Overall, the dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase. It can be seen as a framework for future studies on artificial intelligence techniques for activity recognition, including inertial-only data, visual-only data, or a sensor fusion approach.

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BACKGROUND: Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view. METHODS: Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF). RESULTS: According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution. CONCLUSIONS: RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.

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INTRODUCTION: This article describes an enhanced telepresence robot named ROBIN, part of a telecare system derived from the GIRAFFPLUS project for supporting and monitoring older adults at home. ROBIN is integrated in a sensor-rich environment that aims to continuously monitor physical and psychological wellbeing of older persons living alone. The caregivers (formal/informal) can communicate through it with their assisted persons. Long-term trials in real houses highlighted several user requirements that inspired improvements on the robotic platform. The enhanced telepresence robot was assessed by users to test its suitability to support social interaction and provide motivational feedback on health-related aspects. METHODS: Twenty-five users (n = 25) assessed the new multimodal interaction capabilities and new communication services. A psychophysiological approach was adopted to investigate aspects like engagement, usability, and affective impact, as well as the possible role of individual differences on the quality of human-robot interaction. RESULTS: ROBIN was overall judged usable, the interaction with/through it resulted pleasant and the required workload was limited, thus supporting the idea of using it as a central component for remote assistance and social participation. Open-minded users tended to have a more positive interaction with it. CONCLUSIONS: This work describes an enabling technology for remote assistance and social communication. It highlights the importance of being compliant with users' needs to develop solutions easy to use and able to foster their social connections. The role of personality appeared to be relevant for the interaction, underscoring a clear role of the service personalization.

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BACKGROUND: Hysteroscopy is increasingly performed in an outpatient setting. Pain is the primary reason for abandonment of procedure or incomplete assessment. There is no consensus upon routine use of analgesia during hysteroscopy. OBJECTIVES: To assess the effectiveness and safety of pharmacological interventions for pain relief in women undergoing outpatient hysteroscopy, compared with placebo, no treatment or other pharmacological therapies. SEARCH METHODS: In September 2016 we searched the Cochrane Gynaecology and Fertility (CGF) Trials Register, CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL and two trials registers (ClinicalTrials.gov and WHO ICTRP), together with reference checking and contact with study authors and experts. SELECTION CRITERIA: We included randomised controlled trials (RCTs) comparing use of pharmacological interventions with other pharmacological interventions and pharmacological interventions versus placebo or no treatment. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures expected by Cochrane. Our primary outcome was mean pain score. MAIN RESULTS: We included 32 RCTS (3304 participants), of which only 19 reported data suitable for analysis. Most studies were at unclear or high risk of bias in most of the domains assessed. The evidence was low or very low quality, mainly due to risk of bias and imprecision. Baseline pain scores were relatively low in all groups. Analgesic versus placebo or no treatment Local anaesthetics Local anaesthetics reduced mean pain scores during the procedure [(SMD) -0.29, 95% CI -0.39 to -0.19, 10 RCTs, 1496 women, I2 = 80%, low-quality evidence)] and within 30 minutes (SMD 0.50, 95% CI -0.67 to -0.33, 5 RCTs, 545 women, I2 = 43%, low-quality evidence). This translates to a difference of up to 7 mm on a 0-10 cm visual analogue scale (VAS) during the procedure and up to 13 mm within 30 minutes, which is unlikely to be clinically meaningful. There was no clear evidence of a difference between the groups in mean pain scores after > 30 minutes (SMD -0.11, 95% CI -0.30 to 0.07, 4 RCTs, 450 women, I2 = 0%, low-quality evidence), or in rates of vasovagal reactions (OR 0.70, 95% CI 0.43 to 1.13, 8 RCTs, 1309 women, I2 = 66%, very low-quality evidence). There was insufficient evidence to determine whether there was a difference in rates of non-pelvic pain (OR 1.76, 95% CI 0.53 to 5.80, 1 RCT, 99 women, very low-quality evidence). Nonsteroidal anti-inflammatory drugs (NSAIDs) There was insufficient evidence to determine whether there was a difference between the groups in mean pain scores during the procedure (SMD -0.18, 95% CI -0.35 to 0.00, 3 RCTs, 521 women, I2 = 81%, low-quality evidence). Pain scores were lower in the NSAIDs group within 30 minutes (SMD -0.25, 95% CI -0.46 to -0.04, 2 RCTs, 340 women, I2=29%, low-quality evidence) and at over 30 minutes (SMD -0.27, 95% CI -0.49 to -0.05, 2 RCTs, 321 women, I2 = 78%, low-quality evidence). This equates to maximum differences of under 7.5 mm on a 0-10 cm scale, which are unlikely to be clinically significant. One RCT (181 women) reported adverse events: there was insufficient evidence to determine whether there was a difference between the groups in vasovagal reactions (OR 0.76, 95% CI 0.20 to 2.94, very low-quality evidence). For other reported adverse events (non pelvic pain and allergic reactions) evidence was lacking. Opioids One RCT utilised sublingual buprenorphine and one utilised oral tramadol. Data on pain scores during the procedure were unsuitable for pooling due to inconsistency. Tramadol was associated with a benefit of up to 22 mm on a 0-10 cm scale (SMD -0.76, 95% CI -1.10 to -0.42, 1 RCT, 140 women). However, the effect estimate for this outcome for sublingual opioids did not support a benefit from the intervention (SMD 0.08, 95% CI -0.22 to 0.39, 164 women). Compared with placebo, the pain score within 30 minutes of the procedure was reduced in the tramadol group, with a difference of up to 17mm on a 0-10cm scale (SMD -0.57, 95% CI -0.91 to -0.23 , 1 RCT, 140 women, low-quality evidence. There was no clear evidence of a difference between the tramadol and placebo groups at over 30 minutes (SMD -0.17, 95% CI -0.51 to 0.16, 1 RCT, 140 women, low-quality evidence). Nausea and vomiting occurred in 39% of the buprenorphine group, and in none of the placebo group (OR 107.55, 95% CI 6.44 to 1796.46) Analgesic versus any other analgesic Some comparisons did not report pain scores at all time frames of interest, and none reported data on adverse events. One RCT (84 women) compared local intracervical anaesthesia versus combined intracervical and paracervical anaesthesia. Pain scores were higher in the group with local intracervical anaesthesia during the procedure (SMD 4.27, 95% CI 3.49 to 5.06, very low-quality evidence), within 30 minutes (SMD 1.55, 95% CI 1.06 to 2.05, very low-quality evidence) and at more than 30 minutes (SMD 3.47, 95% CI 2.78 to 4.15, very low-quality evidence). This translates to a possible benefit in the combined group of up to 12 mm on a 0-10 cm scale during the procedure. Benefits at longer follow-up were smaller. One RCT compared antispasmodic + NSAID versus local paracervical anaesthesia. Pain scores were lower in the NSAID group than in the local anaesthesia group (during procedure: SMD -1.40, 95% CI -1.90 to -0.91; >30 minutes after procedure: SMD -0.87, 95% CI -1.33 to -0.41; 80 women, very low-quality evidence). This suggests a possible benefit of during the procedure of up to 23 mm on a 0-10 VAS scale and up to 11 mm >30 minutes after the procedure. Other comparisons included local intracervical anaesthesia versus combined intracervical, paracervical and topical anaesthesia, and opioid versus NSAIDs. Findings were inconclusive. AUTHORS' CONCLUSIONS: There was no consistent good-quality evidence of a clinically meaningful difference in safety or effectiveness between different types of pain relief compared with each other or with placebo or no treatment in women undergoing outpatient hysteroscopy.

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Indication for prostate biopsy is presently mainly based on prostate-specific antigen (PSA) serum levels and digital-rectal examination (DRE). In view of the unsatisfactory accuracy of these two diagnostic exams, research has focused on novel markers to improve pre-biopsy prostate cancer detection, such as phi and PCA3. The purpose of this prospective study was to assess the diagnostic accuracy of phi and PCA3 for prostate cancer using biopsy as gold standard. Phi index (Beckman coulter immunoassay), PCA3 score (Progensa PCA3 assay) and other established biomarkers (tPSA, fPSA and %fPSA) were assessed before a 18-core prostate biopsy in a group of 251 subjects at their first biopsy. Values of %p2PSA and phi were significantly higher in patients with PCa compared with PCa-negative group (p<0.001) and also compared with high grade prostatic intraepithelial neoplasia (HGPIN) (p<0.001). PCA3 score values were significantly higher in PCa compared with PCa-negative subjects (p<0.001) and in HGPIN vs PCa-negative patients (p<0.001). ROC curve analysis showed that %p2PSA, phi and PCA3 are predictive of malignancy. In conclusion, %p2PSA, phi and PCA3 may predict a diagnosis of PCa in men undergoing their first prostate biopsy. PCA3 score is more useful in discriminating between HGPIN and non-cancer.

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