Exploring Human attitude during Human-Robot Interaction

The aim of this work is to provide an automatic analysis to assess the user attitude when interacts with a companion robot. In detail, our work focuses on defining which combination of social cues the robot should recognize so that to stimulate the ongoing conversation and how. The analysis is performed on video recordings of 9 elderly users. From each video, low-level descriptors of the behavior of the user are extracted by using open-source automatic tools to extract information on the voice, the body posture, and the face landmarks. The assessment of 3 types of attitude (neutral, positive and negative) is performed through 3 machine learning classification algorithms: k-nearest neighbors, random decision forest and support vector regression. Since intra- and intersubject variability could affect the results of the assessment, this work shows the robustness of the classification models in both scenarios. Further analysis is performed on the type of representation used to describe the attitude. A raw and an auto-encoded representation is applied to the descriptors. The results of the attitude assessment show high values of accuracy (>0.85) both for unimodal and multimodal data. The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly

How E- learning could enhance coach education programmes

Economic evaluations likely undervalue the benefits of interventions in populations receiving both health and social services, such as frail elderly, by measuring only health-related quality of life. For this reason, alternative preference-based instruments have been developed for economic evaluations in the elderly, such as the ICECAP-O. The aim of this paper is twofold: (1) to evaluate the cost-effectiveness using a short run time frame for an integrated care model for frail elderly, and (2) to investigate whether using a broader measure of (capability) wellbeing in an economic evaluation leads to a different outcome in terms of cost-effectiveness. We performed univariate and multivariate analyses on costs and outcomes separately. We also performed incremental net monetary benefit regressions using quality adjusted life years (QALYs) based on the ICECAP-O and EQ-5D. In terms of QALYs as measured with the EQ-5D and the ICECAP-O, there were small and insignificant differences between the instruments, due to negligible effect size. Therefore, widespread implementation of the Walcheren integrated care model would be premature based on these results. All results suggest that, using the ICECAP-O, the intervention has a higher probability of cost-effectiveness than with the EQ-5D at the same level of WTP. In case an intervention's health and wellbeing effects are not significant, as in this study, using the ICECAP-O will not lead to a false claim of cost-effectiveness of the intervention. On the other hand, if differences in capability QALYs are meaningful and significant, the ICECAP-O may have the potential to measure broader outcomes and be more sensitive to differences between intervention and comparators

Exploring Human attitude during Human-Robot Interaction

The aim of this work is to provide an automatic analysis to assess the user attitude when interacts with a companion robot. In detail, our work focuses on defining which combination of social cues the robot should recognize so that to stimulate the ongoing conversation and how. The analysis is performed on video recordings of 9 elderly users. From each video, low-level descriptors of the behavior of the user are extracted by using open-source automatic tools to extract information on the voice, the body posture, and the face landmarks. The assessment of 3 types of attitude (neutral, positive and negative) is performed through 3 machine learning classification algorithms: k-nearest neighbors, random decision forest and support vector regression. Since intra- and intersubject variability could affect the results of the assessment, this work shows the robustness of the classification models in both scenarios. Further analysis is performed on the type of representation used to describe the attitude. A raw and an auto-encoded representation is applied to the descriptors. The results of the attitude assessment show high values of accuracy (>0.85) both for unimodal and multimodal data. The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly

Exploring Human attitude during Human-Robot Interaction

The aim of this work is to provide an automatic analysis to assess the user attitude when interacts with a companion robot. In detail, our work focuses on defining which combination of social cues the robot should recognize so that to stimulate the ongoing conversation and how. The analysis is performed on video recordings of 9 elderly users. From each video, low-level descriptors of the behavior of the user are extracted by using open-source automatic tools to extract information on the voice, the body posture, and the face landmarks. The assessment of 3 types of attitude (neutral, positive and negative) is performed through 3 machine learning classification algorithms: k-nearest neighbors, random decision forest and support vector regression. Since intra- and intersubject variability could affect the results of the assessment, this work shows the robustness of the classification models in both scenarios. Further analysis is performed on the type of representation used to describe the attitude. A raw and an auto-encoded representation is applied to the descriptors. The results of the attitude assessment show high values of accuracy (>0.85) both for unimodal and multimodal data. The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly

Moving Beyond the Status Quo of Integrated Inpatient Medical and Psychiatric Care Units: The Path to Real-World Evaluation

Integrated inpatient medical and psychiatric care units (IMPUs) are hospital wards that care for inpatients with both acute general medical and psychiatric disorders. IMPU development has stalled, and wide variation in IMPU designs may reflect the fact that IMPUs are still in an early evolutionary stage. High-quality evidence concerning the costs and effectiveness of IMPUs is sparse, because IMPUs do not lend themselves well to traditional evidence-based medicine methods. As a result, most studies of IMPUs have been only observational. Therefore, it is time for a different approach, in which goals for IMPUs are explicitly formulated and IMPU research is incorporated into evidence-based practice (EBP) instead of evidence-based medicine. EBP can be viewed as integrating best available evidence into organizational practices by using four pillars of evidence: organizational, experiential, stakeholder, and scientific. Such types of evidence require an investment in describing the field more precisely. When pragmatic reasoning, where clinical expertise and organizational needs determine IMPU designs, is replaced with EBP, researchers can more effectively perform studies that may convince health care policy makers that IMPUs represent a cost-effective way to improve patients' health and that they increase the well-being of both patients and hospital staff

Additional file 2: of The frail older person does not exist: development of frailty profiles with latent class analysis

Table S2. Conditional probabilities per profile. (DOCX 15 kb

Additional file 3: of The frail older person does not exist: development of frailty profiles with latent class analysis

Focus group protocol. (DOCX 20 kb