Supervising and improving attentiveness in human computer interaction

The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Learning frequent behaviors patterns in intelligent environments for attentiveness level

Nowadays, when it comes to achieving goals in business environments or educational environments, the performance successfully has an important role in performing a task. However, this performance can be affected by several factors. One of the most common is the lack of attention. The individual’s attention in performing a task can be determinant for the final quality or even at the task’s conclusion. In this paper is intended to design a solution that can reduce or even eliminate the lack of attention on performing a task. The idea consist on develop an architecture that capture the user behavior through the mouse and keyboard usage. Furthermore, the system will analyze how the devices are used.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Motion and emotion estimation for robotic autism intervention.

Robots have recently emerged as a novel approach to treating autism spectrum disorder (ASD). A robot can be programmed to interact with children with ASD in order to reinforce positive social skills in a non-threatening environment. In prior work, robots were employed in interaction sessions with ASD children, but their sensory and learning abilities were limited, while a human therapist was heavily involved in “puppeteering” the robot. The objective of this work is to create the next-generation autism robot that includes several new interactive and decision-making capabilities that are not found in prior technology. Two of the main features that this robot would need to have is the ability to quantitatively estimate the patient’s motion performance and to correctly classify their emotions. This would allow for the potential diagnosis of autism and the ability to help autistic patients practice their skills. Therefore, in this thesis, we engineered components for a human-robot interaction system and confirmed them in experiments with the robots Baxter and Zeno, the sensors Empatica E4 and Kinect, and, finally, the open-source pose estimation software OpenPose. The Empatica E4 wristband is a wearable device that collects physiological measurements in real time from a test subject. Measurements were collected from ASD patients during human-robot interaction activities. Using this data and labels of attentiveness from a trained coder, a classifier was developed that provides a prediction of the patient’s level of engagement. The classifier outputs this prediction to a robot or supervising adult, allowing for decisions during intervention activities to keep the attention of the patient with autism. The CMU Perceptual Computing Lab’s OpenPose software package enables body, face, and hand tracking using an RGB camera (e.g., web camera) or an RGB-D camera (e.g., Microsoft Kinect). Integrating OpenPose with a robot allows the robot to collect information on user motion intent and perform motion imitation. In this work, we developed such a teleoperation interface with the Baxter robot. Finally, a novel algorithm, called Segment-based Online Dynamic Time Warping (SoDTW), and metric are proposed to help in the diagnosis of ASD. Social Robot Zeno, a childlike robot developed by Hanson Robotics, was used to test this algorithm and metric. Using the proposed algorithm, it is possible to classify a subject’s motion into different speeds or to use the resulting SoDTW score to evaluate the subject’s abilities

Nothing Recedes Like Success - Risk Analysis and the Organizational Amplification of Risks

Professor Freudenburg believes that there is room for improvement in Risk analysis, particularly in drawing on systematic studies of human behavior in the calculation of real, empirical probabilities of failure. The need is argued to be especially acute where technological Risks are associated with low expected probabilities of failure and are managed by human organizations for extended periods of time. This permits complacency to set in

Reading between the Lines. An ethnographic field study on personalization in providing e-therapy

E-therapy is an important and increasing care practice in mental healthcare. This article explores possibilities and shortcomings of personalization in providing e-therapy for patients suffering from Cancer-Related Fatigue (CRF). The main question of this study is as follows: How do online therapists personalize their care to individual patients in providing e-therapy? To answer this question, an ethnographic field study was carried out on an online Mindfulness-Based Cognitive Therapy (MBCT) for CRF. In doing so, the work practices of online therapists were observed, online correspondence was studied, interviews were conducted, and a meeting of online therapists was recorded and studied. This study resulted in a better understanding of the structure, as well as the possibilities and the limitations of personalization in text-based e-therapy. The results show that the online MBCT potentially provides attuned, and also bodily attentive, care. However, in dealing with difficulties like asynchrony and invisibility, therapists also face limitations of personalization in their practices. Especially when patients fail to provide self-disclosure, the therapist may have insufficient information to act adequately and to prevent patients from dropping out

SIMULATED MEDICAL ENCOUNTERS TO ANALYZE PATIENT-PHYSICIAN COMMUNICATION DURING ELECTRONIC MEDICAL RECORDS\u27 USE IN PRIMARY CARE

The implications of the patient-physician relationship and communication on healthcare quality have been widely discussed in previous research. Communication has been characterized as one of the most powerful, encompassing, and versatile instruments available to the physician and it has been suggested that good patient-physician communication can improve healthcare outcomes. The incorporation of Electronic Medical Records (EMRs) in primary care provides an opportunity for improving healthcare services and quality of care. EMRs have, without a doubt, transformed the dynamics of the medical encounter. Implications of EMRs on the patient-physician communication, and thus on healthcare quality, have not yet reached a full understanding. Existing physician communication skills assessment tools do not take into account the physician\u27s need to divert his/her attention from the patient to the computer, and vise versa. One such tool is the SEGUE. This research-in-progress paper aims to describe the preliminary steps taken to assess the adequacy of the existing SEGUE tool in evaluating physicians\u27 communication skills in a computerized environment based on simulated medical encounters. Assuming that the existing SEGUE tool does not capture the new dynamics of the medical encounter; we suggest that it should be enhanced to include best-practices for physicians\u27 EMR use while maintaining effective communication with patients. We intend to develop a set of items which reflect recommendations for EMR use aimed at maintaining effective communication with the patient. These new items will be formulated based on an extant literature review and experts panel, and will eventually be incorporated into the existing SEGUE tool to provide a comprehensive tool for analyzing physicians\u27 communication skills in the computerized clinic

How language of interaction affects the user perception of a robot

Spoken language is the most natural way for a human to communicate with a robot. It may seem intuitive that a robot should communicate with users in their native language. However, it is not clear if a user's perception of a robot is affected by the language of interaction. We investigated this question by conducting a study with twenty-three native Czech participants who were also fluent in English. The participants were tasked with instructing the Pepper robot on where to place objects on a shelf. The robot was controlled remotely using the Wizard-of-Oz technique. We collected data through questionnaires, video recordings, and a post-experiment feedback session. The results of our experiment show that people perceive an English-speaking robot as more intelligent than a Czech-speaking robot (z = 18.00, p-value = 0.02). This finding highlights the influence of language on human-robot interaction. Furthermore, we discuss the feedback obtained from the participants via the post-experiment sessions and its implications for HRI design.Comment: ICSR 202

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio