Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia : A Case Study

Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API© were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API© are useful for creating future multimodal emotional data about robot–patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human–robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API© were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia

Conversational affective social robots for ageing and dementia support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation

An Intervening Ethical Governor for a Robot Mediator in Patient-Caregiver Relationships

© Springer International Publishing AG 2015DOI: 10.1007/978-3-319-46667-5_6Patients with Parkinson’s disease (PD) experience challenges when interacting with caregivers due to their declining control over their musculature. To remedy those challenges, a robot mediator can be used to assist in the relationship between PD patients and their caregivers. In this context, a variety of ethical issues can arise. To overcome one issue in particular, providing therapeutic robots with a robot architecture that can ensure patients’ and caregivers’ dignity is of potential value. In this paper, we describe an intervening ethical governor for a robot that enables it to ethically intervene, both to maintain effective patient–caregiver relationships and prevent the loss of dignity

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure

Patient Relationship Management (PRM) and AI: The role of Affective Computing

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceArtificial Intelligence (AI) has been praised as the next big thing in the computing revolution, and it's been touted as a game-changer in a variety of fields, including healthcare. The increasing popularity of this technology is driving early adoption and leading to a lack of consideration of the patient perspective in its use, bringing new sources of distrust that come from the absence of human attributes. This study aims to address this problem by presenting a strategy in the area of affective computing that will combat this absence of empathy experienced by the patient during a medical process. To reach this goal, a Design Science Research Methodology will be followed. A preliminary literature study had already been completed, and the research topic and objectives had been established. In addition, to apply the artifact developments, a bot will be built and evaluated by a set of users. The increased awareness of these AI systems will, expectedly, stimulate their use. By adding new research into the affective computing field, it is also expected to contribute to the digital healthcare evolution and to encourage further scientific progress in this area

Facial Expression Rendering in Medical Training Simulators: Current Status and Future Directions

Recent technological advances in robotic sensing and actuation methods have prompted development of a range of new medical training simulators with multiple feedback modalities. Learning to interpret facial expressions of a patient during medical examinations or procedures has been one of the key focus areas in medical training. This paper reviews facial expression rendering systems in medical training simulators that have been reported to date. Facial expression rendering approaches in other domains are also summarized to incorporate the knowledge from those works into developing systems for medical training simulators. Classifications and comparisons of medical training simulators with facial expression rendering are presented, and important design features, merits and limitations are outlined. Medical educators, students and developers are identified as the three key stakeholders involved with these systems and their considerations and needs are presented. Physical-virtual (hybrid) approaches provide multimodal feedback, present accurate facial expression rendering, and can simulate patients of different age, gender and ethnicity group; makes it more versatile than virtual and physical systems. The overall findings of this review and proposed future directions are beneficial to researchers interested in initiating or developing such facial expression rendering systems in medical training simulators.This work was supported by the Robopatient project funded by the EPSRC Grant No EP/T00519X/