Showing papers by "Katarzyna Wac published in 2019"

Multimodal Integration of Emotional Signals from Voice, Body, and Context: Effects of (In)Congruence on Emotion Recognition and Attitudes Towards Robots

Abstract: Humanoid social robots have an increasingly prominent place in today’s world. Their acceptance in social and emotional human–robot interaction (HRI) scenarios depends on their ability to convey well recognized and believable emotional expressions to their human users. In this article, we incorporate recent findings from psychology, neuroscience, human–computer interaction, and HRI, to examine how people recognize and respond to emotions displayed by the body and voice of humanoid robots, with a particular emphasis on the effects of incongruence. In a social HRI laboratory experiment, we investigated contextual incongruence (i.e., the conflict situation where a robot’s reaction is incongrous with the socio-emotional context of the interaction) and cross-modal incongruence (i.e., the conflict situation where an observer receives incongruous emotional information across the auditory (vocal prosody) and visual (whole-body expressions) modalities). Results showed that both contextual incongruence and cross-modal incongruence confused observers and decreased the likelihood that they accurately recognized the emotional expressions of the robot. This, in turn, gives the impression that the robot is unintelligent or unable to express “empathic” behaviour and leads to profoundly harmful effects on likability and believability. Our findings reinforce the need of proper design of emotional expressions for robots that use several channels to communicate their emotional states in a clear and effective way. We offer recommendations regarding design choices and discuss future research areas in the direction of multimodal HRI.

Acute mental stress and surgical performance

Abstract: Background Stress has been shown to impact adversely on multiple facets critical to optimal performance. Advancements in wearable technology can reduce barriers to observing stress during surgery. This study aimed to investigate the association between acute intraoperative mental stress and technical surgical performance. Methods Continuous electrocardiogram data for a single attending surgeon were captured during surgical procedures to obtain heart rate variability (HRV) measures that were used as a proxy for acute mental stress. Two different measures were used: root mean square of successive differences (RMSSD) and standard deviation of RR intervals (SDNN). Technical surgical performance was assessed on the Operating Room Black Box® platform using the Generic Error Rating Tool (GERT). Both HRV recording and procedure video recording were time-stamped. Surgical procedures were fragmented to non-overlapping intervals of 1, 2 and 5 min, and subjected to data analysis. An event was defined as any deviation that caused injury to the patient or posed a risk of harm. Results Rates of events were significantly higher (47-66 per cent higher) in the higher stress quantiles than in the lower stress quantiles for all measured interval lengths using both proxy measures for acute mental stress. The strongest association was observed using 1-min intervals with RMSSD as the HRV measure (P < 0·001). Conclusion There is an association between measures of acute mental stress and worse technical surgical performance. Further study will help delineate the interdependence of these variables and identify triggers for increased stress levels to improve surgical safety.

Assessment of menstrual health status and evolution through mobile apps for fertility awareness.

Abstract: For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual’s reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women’s health overall, which has historically been severely understudied.

“I Must Try Harder”: Design Implications for Mobile Apps and Wearables Contributing to Self-Efficacy of Patients With Chronic Conditions

Abstract: Background Diverse wellness-promoting mobile health technologies, including mobile apps and wearable trackers, became increasingly popular due to their ability to support patients' self-management of health conditions. However, the patient's acceptance and use depend on the perceived experience and the app appropriateness to the patient's context and needs. We have some understating of the experience and factors influencing the use of these technologies in the general public, but we have a limited understanding of these issues in patients. Objective By presenting results from an explorative study, this paper aims to identify implications for the design of mobile apps and wearables to effectively support patients' efforts in self-management of health with a special emphasis on support for self-efficacy of activities contributing to health. Methods An explorative mixed-method study involving 200 chronically ill patients of Stanford Medical Center (Stanford, CA, United States) was conducted between mid-2016 and end of 2018. Amongst these, 20 patients were involved in a 4-weeks study, in which we collected the underlying wearable device use logs (e.g., Fitbit) and subjective use experience [via an Ecological Momentary Assessment (EMA)], as well as patients' momentary perception of general self-efficacy in their natural environments and different daily contexts. Results The results indicate that mobile apps for health and wearables have the potential to enable better self-management and improve patients' wellbeing but must be further refined to address different human aspects of their use. Specifically, the apps/wearables should be easier to use, more personalized and context-aware for the patient's overall routine and lifestyle choices, as well as with respect to the momentary patient state (e.g., location, type of people around) and health(care) needs. Additionally, apps and devices should be more battery efficient and accurate; providing timely, non-judgmental feedback and personalized advice to the patients anywhere-anytime-anyhow. These results are mapped on major sources of the individuals' self-efficacy. Conclusion Our results show how the apps/wearables that are aimed at supporting the patients' self-management should be designed to leverage and further improve the patients' general self-efficacy and self-efficacy of activities contributing to chronic disease management.

Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm.

Abstract: Background: Smartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals’ lifestyle and sleep patterns Objectives: The objective of this study was to estimate sleep duration based on the analysis of the users’ ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm Methods: We used smartwatch sleep assessment data as the ground truth Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each Results: Results showed that based on the smartphone ON-OFF patterns, individual’s sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns Conclusions: It is possible to estimate sleep duration patterns using only data related to smartphone screen interaction

Reprocessing and reuse of single-use medical devices: perceptions and concerns of relevant stakeholders toward current practices.

Abstract: Background:Research and history have largely shown the covert billion-dollar global market of single-use medical device (SUD) reprocessing and reuse to be a safe endeavor, but awareness and perceptions of the practice both within and outside of healthcare have received limited attention.Meth

Predicting Quality of Experience of Popular Mobile Applications from a Living Lab Study

Abstract: In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications’ QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94 0.77 accuracy. Surprisingly, after the following top three features:± session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.

LDC '19: international workshop on longitudinal data collection in human subject studies

Abstract: Individuals increasingly use mobile, wearable, and ubiquitous devices capable of unobtrusive collection of vast amounts of scientifically rich personal data over long periods (months to years), and in the context of their daily life. However, numerous human and technological factors challenge longitudinal data collection, often limiting research studies to very short data collection periods (days to weeks), spawning recruitment biases, and affecting participant retention over time. This workshop is designed to bring together researchers involved in longitudinal data collection studies to foster an insightful exchange of ideas, experiences, and discoveries to improve the studies' reliability, validity, and perceived meaning of longitudinal mobile, wearable, and ubiquitous data collection for the participants.

Towards Personalizing Participation in Health Studies

Abstract: There is substantial evidence on the relevant factors that motivate participation in human subject studies and the expectations of participants when sharing their health data for research. However, most human subject studies focus on participant eligibility and data collection, omitting even a rudimentary use of the factors that motivate participation. We illustrate an approach to use motivation to construct personalized stories and exemplify it by using a chatbot under development towards monitoring, analyzing, and influencing health study participation, engagement, and retention. Additionally, we discuss the new advantages, challenges, and unexplored avenues for research stemming from our approach.