Impact of Distance and Movement Speed on the Acceptance of Human-Robot Interaction – Method and First Evaluation

The number of scenarios where an interaction between humans and robots is part of the everyday life increased constantly during the last years. Therefore, it is important to focus on a good interaction between both parts, the humans and the robots, as well as the absence of negative emotions. Especially, emotions like fear and anxiety are of great interest. The presented study focuses on a first concept of measuring these emotions and the acceptance through a multidimensional approach. A simple handover task was chosen for the collaboration. Different motion speeds of the robot as well as distances between the robot and the human were considered. Moreover, the impact of two different interaction heights, at face level or at chest level, was examined. In addition to the subjective assessment of the participants, psychophysiological parameters (cardiovascular and electrodermal activity) were recorded during the human-robot interaction. The concept was first evaluated with a number of four participants, limited by governmental restrictions due to the current COVID-19 pandemic situation. The results proof the success of the chosen procedure. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Reading the Room - Automated, Momentary Assessment of Student Engagement in the Classroom: Are We There Yet?

When in front of a classroom, a skilled teacher can read the room, identifying when students are engaged, frustrated, distracted, etc. In recent years we have seen significant changes in the traditional classroom, with virtual classes becoming a normal learning environment. Reasons for this change are the increased popularity of Massive Open Online Courses (MOOCs) and the disruptions imposed by the ongoing COVID-19 pandemic. However, it is difficult for teachers to read the room in these virtual classrooms, and researchers have begun to look at using sensors to provide feedback to help inform teaching practices. The study presented here sought to ground classroom sensor data in the form of electrodermal activities (EDA) captured using a wrist-worn sensing platform (Empatica E4), with observations about students' emotional engagement in the class. We collected a dataset from eleven students over eight lectures in college-level computer science classes. We trained human annotators who provided ground truth information about student engagement based on in-class observations. Inspired by related work in the field, we implemented an automated data analysis framework, which we used to explore momentary assessments of student engagement in classrooms. Our findings surprised us because we found no significant correlation between the sensor data and our trained observers' data. In this paper, we present our study and framework for automated engagement assessment, and report on our findings that indicate some of the challenges in deploying current technology for real-world, automated momentary assessment of student engagement in the classroom. We offer reflections on our findings and discuss ways forward toward an automated reading the room approach.

MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals.

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

Distributed Remote Psychophysiological Data Collection for UX Evaluation: A Pilot Project

User experience (UX) research has been critically impacted by the recent COVID-19 pandemic and the sanitary restrictions put in place. Observational or perceptual studies can be adapted remotely with participants using their own computer and internet access. However, studies based on the unconscious and automatic physiological states of participants use neurophysiological measurements that requires highly specific hardware. Electrodermal activity (EDA) or electrocardiogram (ECG) based studies are complex to transpose to a remote environment since researchers have no physical contact with the participants. To address this concern, our research team previously developed a remote instrument that can collect the EDA and the ECG activity at the participants' location through a moderated self-installation of sensors. We developed a protocol for remote physiological data collection that we pilot tested with 2 UX studies. After each study, we administered an open-ended questionnaire regarding the full experience of remote data-collection from both the moderator's and the participant's side. We collected 92 responses total which provided us with a rich dataset that we analyzed through a thematic analysis lens in order to uncover the success factors of remote psychophysiological data collection. Operational support, moderator-participant collaboration, individual characteristics, and technological capabilities clearly emerged as drivers for success. This project aimed to develop a rigorous and contextually relevant protocol for remote physiological data collection in UX evaluations, train our research team on the developed protocol, and provide guidance regarding remote physiological data collection activities.

Effects of The Long-Time Wearing of Gloves by Health Care Workers Managing Covid-19 Assessed by Electrodermal Activity: A Pilot Study

Continues wearing gloves is a crucial problem among health care workers treating patients with coronavirus (COVID-19) infection that may cause skin complications. In the present study, the skin conductance (SC), skin potential (SP) and skin susceptance (SS) were used to identify the effects of long-term wearing gloves among health care workers managing COVID-19. The experimental procedure was repeated 30 times on three subjects recruited as volunteers to wear gloves for 4 hours. The baseline of parameters SC, SP, and SS were recorded at normal conditions without wearing the glove at zero time (To). The parameters were again recorded in the cases of glove-wearing with and without zinc oxide ointment (ZnO) for the same period. Noticeable differences were found in each of SC, SP and SS for subjects who wore gloves when compared to the baseline for 4 h. Results also showed significant differences in each of SP and SS during the same period with applying ZnO comparing to wearing gloves without applying ZnO. The results show that skin hydration is significantly improved by using ZnO and changing gloves at least every four hours of continuous wearing is recommended. However, due to the small sample size and the specific study population, the results cannot be generalized to all ages of health workers.

Pre-pandemic electrodermal activity predicts current COVID-related fears: household size during lockdown as a moderating factor.

Background: Despite the immense impact of COVID-19 on mental health, there is a lack of prospective studies examining physiological predictors of current risk factors. Moreover, although physiological processes evidently interact with socio-demographic factors to modulate individuals' response to a crisis, it remains largely unknown how these complex interactions shape people's mental responses to COVID-19. To fill these gaps of knowledge, we chose a potent physiological marker of distress - heightened baseline electrodermal activity (EDA) measured before the pandemic began - and hypothesized it would be related to greater COVID-related fears and worries as a function of individuals' household size.Method: 185 individuals (71% women), who had participated in our lab studies 2-3 years ago, in which we assessed their baseline EDA, completed several questionnaires online, including assessments of their current fears regarding COVID. Participants also reported the number of people in their household, with whom they had been together during a lockdown which was taking place at the time. We used pre-pandemic EDA measures in combination with their household size to predict participants' current fears.Results: Pre-pandemic EDA measures predicted current COVID-related fears and worries. Specifically for the EDA measure "number of skin conductance responses", we further found that the number of people in the household during the lockdown, moderated the abovementioned relationship, such that it occurred in individuals with average and larger households and not in those with small households.Conclusions: We provide a highly relevant and unique combination of physiological, socio-demographic, and psychological measures, which augments the potential to optimally target populations vulnerable to COVID-related distress, and subsequently offer them early mental health interventions.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress.

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.