492 research outputs found
A Spark Of Emotion: The Impact of Electrical Facial Muscle Activation on Emotional State and Affective Processing
Facial feedback, which involves the brain receiving information about the activation of facial muscles, has the potential to influence our emotional states and judgments. The extent to which this applies is still a matter of debate, particularly considering a failed replication of a seminal study. One factor contributing to the lack of replication in facial feedback effects may be the imprecise manipulation of facial muscle activity in terms of both degree and timing. To overcome these limitations, this thesis proposes a non-invasive method for inducing precise facial muscle contractions, called facial neuromuscular electrical stimulation (fNMES). I begin by presenting a systematic literature review that lays the groundwork for standardising the use of fNMES in psychological research, by evaluating its application in existing studies. This review highlights two issues, the lack of use of fNMES in psychology research and the lack of parameter reporting. I provide practical recommendations for researchers interested in implementing fNMES. Subsequently, I conducted an online experiment to investigate participants' willingness to participate in fNMES research. This experiment revealed that concerns over potential burns and involuntary muscle movements are significant deterrents to participation. Understanding these anxieties is critical for participant management and expectation setting. Subsequently, two laboratory studies are presented that investigated the facial FFH using fNMES. The first study showed that feelings of happiness and sadness, and changes in peripheral physiology, can be induced by stimulating corresponding facial muscles with 5âseconds of fNMES. The second experiment showed that fNMES-induced smiling alters the perception of ambiguous facial emotions, creating a bias towards happiness, and alters neural correlates of face processing, as measured with event-related potentials (ERPs). In summary, the thesis presents promising results for testing the facial feedback hypothesis with fNMES and provides practical guidelines and recommendations for researchers interested in using fNMES for psychological research
Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks
Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workersâ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subjectâs physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches
Data-Driven Evaluation of In-Vehicle Information Systems
Todayâs In-Vehicle Information Systems (IVISs) are featurerich systems that provide the driver with numerous options for entertainment, information, comfort, and communication. Drivers can stream their favorite songs, read reviews of nearby restaurants, or change the ambient lighting to their liking. To do so, they interact with large center stack touchscreens that have become the main interface between the driver and IVISs. To interact with these systems, drivers must take their eyes off the road which can impair their driving performance. This makes IVIS evaluation critical not only to meet customer needs but also to ensure road safety. The growing number of features, the distraction caused by large touchscreens, and the impact of driving automation on driver behavior pose significant challenges for the design and evaluation of IVISs. Traditionally, IVISs are evaluated qualitatively or through small-scale user studies using driving simulators. However, these methods are not scalable to the growing number of features and the variety of driving scenarios that influence driver interaction behavior. We argue that data-driven methods can be a viable solution to these challenges and can assist automotive User Experience (UX) experts in evaluating IVISs. Therefore, we need to understand how data-driven methods can facilitate the design and evaluation of IVISs, how large amounts of usage data need to be visualized, and how drivers allocate their visual attention when interacting with center stack touchscreens.
In Part I, we present the results of two empirical studies and create a comprehensive understanding of the role that data-driven methods currently play in the automotive UX design process. We found that automotive UX experts face two main conflicts: First, results from qualitative or small-scale empirical studies are often not valued in the decision-making process. Second, UX experts often do not have access to customer data and lack the means and tools to analyze it appropriately. As a result, design decisions are often not user-centered and are based on subjective judgments rather than evidence-based customer insights. Our results show that automotive UX experts need data-driven methods that leverage large amounts of telematics data collected from customer vehicles. They need tools to help them visualize and analyze customer usage data and computational methods to automatically evaluate IVIS designs.
In Part II, we present ICEBOAT, an interactive user behavior analysis tool for automotive user interfaces. ICEBOAT processes interaction data, driving data, and glance data, collected over-the-air from customer vehicles and visualizes it on different levels of granularity. Leveraging our multi-level user behavior analysis framework, it enables UX experts to effectively and efficiently evaluate driver interactions with touchscreen-based IVISs concerning performance and safety-related metrics.
In Part III, we investigate driversâ multitasking behavior and visual attention allocation when interacting with center stack touchscreens while driving. We present the first naturalistic driving study to assess driversâ tactical and operational self-regulation with center stack touchscreens. Our results show significant differences in driversâ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. These results emphasize the importance of context-dependent driver distraction assessment of driver interactions with IVISs. Motivated by this we present a machine learning-based approach to predict and explain the visual demand of in-vehicle touchscreen interactions based on customer data. By predicting the visual demand of yet unseen touchscreen interactions, our method lays the foundation for automated data-driven evaluation of early-stage IVIS prototypes. The local and global explanations provide additional insights into how design artifacts and driving context affect driversâ glance behavior.
Overall, this thesis identifies current shortcomings in the evaluation of IVISs and proposes novel solutions based on visual analytics and statistical and computational modeling that generate insights into driver interaction behavior and assist UX experts in making user-centered design decisions
Emotion in Motion: Experiment on Affective Responses to Virtual Realities
The rise of metaverse platforms pave the path for a future where many human activities will be conducted in a virtual reality (VR). Therefore, it is necessary to investigate whether and how users emotionally respond to fully digitalized realities when conducting daily activities. In this study, we focus on two core features: immersion and sense of embodiment through self-motion. We conduct a large-scale experiment to examine the effects of virtual realities on emotional traits (PANAS). We use high (VR head-mounted-display) and low (PC monitor) immersive environments, as well as different levels of self-motion (high/low), compared to a 2D control. Students (N=183) were randomly assigned in one of 5 virtual conditions to complete a daily non-emotionally charged task. Two analyses were conducted based on the same experiment. The findings indicate that immersion and embodiment are not direct emotional elicitors on their own, and affect valence and arousal are likely content related.Peer reviewe
Meeting the Psychological Needs of Astronauts in the Flourishing Human Spaceflight Frontier: The Case for Astronaut-Trained Psychologists
Space psychology (i.e., astronaut psychological counseling and support) has remained largely unchanged since the onset of long-duration low-Earth-orbit (LEO) human spaceflight missions, with teletherapy utilized as the primary means of psychotherapy delivery. However, with NASAâs plans to establish a permanent human presence on the Moon, the suitability of teletherapy â as well as astronaut-trained psychologists, an alternative space psychology method suggested for human spaceflight beyond LEO â must be ascertained. The aim of this novel space psychology investigation was to identify and compare the effectiveness of three astronaut psychotherapy treatment conditions (i.e., teletherapy with a 2 second Earth to LEO latency, teletherapy with a 10 second Earth to Moon latency, and in-person astronaut trained psychologist delivered therapy with practically no latency) at reducing stress levels among astronauts/astronaut-surrogates in an analogue human spaceflight environment. 24 screened astronaut-surrogates randomly underwent each of the astronaut psychotherapy treatments, and no astronaut-surrogate received repeated treatments. Stress indicators (i.e., heart rate, blood pressure, and self-reported perceived stress questionnaire scores) were measured at multiple intervals throughout the psychotherapy treatment sessions and were analyzed via repeated measures ANOVA. By all metrics, the astronaut-trained psychologist treatment significantly outperformed both teletherapy treatments at reducing stress; and teletherapy with 10 second latency was deemed unsuitable for astronauts. Thus, astronaut-trained psychologists appear to be the most efficacious feasibly integrable space psychology solution for improving wellbeing and reducing stress among individual astronauts and astronaut crews in future long duration human spaceflight operations and missions beyond LEO (e.g., NASAâs Artemis Lunar mission). Additionally, astronaut-trained psychologists appear to be highly effective when operating in LEO as well, and therefore are also ideal for space tourism and commercial astronaut applications
Ongoing Tracking of Engagement in Motor Learning
Teaching motor skills such as playing music, handwriting, and driving, can
greatly benefit from recently developed technologies such as wearable gloves
for haptic feedback or robotic sensorimotor exoskeletons for the mediation of
effective human-human and robot-human physical interactions. At the heart of
such teacher-learner interactions still stands the critical role of the ongoing
feedback a teacher can get about the student's engagement state during the
learning and practice sessions. Particularly for motor learning, such feedback
is an essential functionality in a system that is developed to guide a teacher
on how to control the intensity of the physical interaction, and to best adapt
it to the gradually evolving performance of the learner. In this paper, our
focus is on the development of a near real-time machine-learning model that can
acquire its input from a set of readily available, noninvasive,
privacy-preserving, body-worn sensors, for the benefit of tracking the
engagement of the learner in the motor task. We used the specific case of
violin playing as a target domain in which data were empirically acquired, the
latent construct of engagement in motor learning was carefully developed for
data labeling, and a machine-learning model was rigorously trained and
validated
Consumer Neuroscience e Brand Relationship: misurare lâassociazione implicita tra il SĂ© del consumatore e il brand.
Il presente elaborato si focalizza sulla connessione tra Consumer Neuroscience e Brand Relationship con un focus specifico sul SĂ© del consumatore, analizzato attraverso uno strumento di misurazione indiretta del comportamento. Lâobiettivo Ăš stato quello di contribuire alla validazione e allâutilizzo nel contesto italiano di un SC-IAT per lo studio dellâassociazione tra SĂ© e brand, interpretandone i risultati tramite unâanalisi di matrice neuroscientifica su stimoli brand-related. Il vantaggio di questo strumento, rispetto allo IAT tradizionale, Ăš quello di poter âfotografareâ unâistantanea sulla relazione senza la necessitĂ di utilizzare una dimensione comparativa. Misurando direttamente la forza dellâassociazione tra il concetto del brand e quello del SĂ©. Per farlo, lâautore Ăš passato attraverso fasi distinte che hanno prima indagato gli aspetti puramente psicometrici dello strumento, per dedicarsi successivamente a un test neuroscientifico. I risultati hanno evidenziato delle buone performance del SC-IAT, cosĂŹ pensato, suggerendo approfondimenti futuri e applicazioni a brand dalla differente architettura. Inoltre, lâanalisi neurofisiologica ha evidenziato come lo strumento possa risultare efficace nel fornire unâinterpretazione aggiuntiva agli indicatori neurofisiologici testati durante la visualizzazione di uno stimolo relativo al brand
Using Music to Modify Step-Rate and Running Biomechanics in Healthy Runners
Context: Running-related injury (RRI) is a significant public health issue that may be caused by injurious running biomechanics. Increasing step-rate (SR) using gait retraining may prevent and treat RRI. The Optimizing Performance Through Intrinsic Motivation and Attention for Learning (OPTIMAL) theory indicates enhanced expectancies, autonomy, and external focus of attention will optimize motor learning. Music has been shown to create enhanced expectancies, can provide incidental choices (autonomy), directs attention externally, and may increase compliance. No studies have investigated if music can be used to alter SR and running biomechanics or strategies that may improve compliance to gait retraining. Objective: The purpose of this study was to 1) compare differences in SR and running biomechanics between those who use music auditory cueing (MUS) and those who use metronome auditory cueing (MET) during the phases of a temporospatial gait retraining protocol, 2) compare differences in RPE change scores across four temporospatial gait retraining sessions between the MUS and MET group, and 3) determine if there is an association between groups (MUS and MET) and compliance to a self-administered, temporospatial gait retraining protocol and describe the likelihood of compliance between groups (MUS and MET).
Methods: Thirty, healthy recreational runners were included and randomly placed in either the MET or MUS group. Inertial measurement unit motion analysis collected SR, peak positive tibial acceleration (PPA), and peak stance phase hip adduction (peakHIPADD) during the stance phase of running. A cellular device application collected running volume and SR data when participants ran outside of the lab, which defined compliance. The Borgâs rate of perceived exertion (RPE) scale was used to compare change in RPE between groups. A multivariate repeated measures ANOVA was used to compare SR, PPA, and peakHIPADD from the introductory pretest (INTROpre) and the three posttests (INTROpost, LABpost, SELFpost). Change scores between baseline RPE and RPE after each gait retraining session were calculated and analyzed using a mixed repeated measures ANOVA. SR and running volume were derived from the cellular application exports and compliance was defined as 1) maintaining an average SR within +/- two steps per minute of the target SR throughout each run and 2) maintaining the average running volume. Runners were assigned as âcompliantâ and ânoncompliantâ. A Fischerâs exact test was performed, and an odds ratio was calculated to determine association and likelihood of compliance between groups. Results: Both groups increased SR between the INTROpre and introductory posttest (INTROpost) (p \u3c.001), and the increase in SR was maintained at all other posttest timepoints (LABpost and SELFpost). There were no differences in PPA or peakHIPADD at any posttest timepoints regardless of group. No significant differences in RPE change scores between groups across time were found. There was a significant association between group and compliance (p = .05) and the MUS group was ~6 times as likely to comply with the self-administered gait retraining program.
Conclusions: SR can be altered using either a metronome or music tempo. Both a metronome and music can be used as an auditory cue without creating increased perception of exertion. Runners using the music auditory cueing may continue to practice their new target SR more than runners assigned a metronome cueing, which provides rationale to use music to retrain SR within a self-administered gait retraining protocol. Running biomechanics may not have changed since SR was only increased by 5% so future research should repeat the current study methods using larger increases in SR
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