Emotion Tracking

Previous literature has examined the significance of emotional consumer experiences increasingly pursued by consumers. However, the current knowledge of emotional responses in real-time and real-world consumption settings is still limited. Emotions have previously been measured with observation, self-report, and physiological methods. Digitalization and technological development have, however, advanced these methods as individuals now engage in various self-tracking practices. The chapter introduces emotion tracking as an additional means for measuring emotions. One application, the Emotion Tracker®, was tested by students (n=19) who tracked their emotions (n=617) related to various consumer experiences and reported their user experiences in research diaries. Emotion tracking facilitated real-time and real-world emotion measurement by partly combining the strengths and diminishing the weaknesses of traditional methods. The future of emotion measurement is likely to lie in the combination of subjective and objective self-tracking practices embedded in individuals' everyday lives.

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Effects of personal relevance and simulated darkness on the affective appraisal of a virtual environment

PeerJ ◽

10.7717/peerj.1743 ◽

2016 ◽

Vol 4 ◽

pp. e1743 ◽

Cited By ~ 2

Author(s):

Alexander Toet ◽

Joske M. Houtkamp ◽

Paul E. Vreugdenhil

Keyword(s):

Virtual Environment ◽

Social Presence ◽

Real World ◽

Emotional Responses ◽

Self Report ◽

Personal Relevance ◽

The Real ◽

Ambient Light Level ◽

Ambient Lighting

This study investigated whether personal relevance influences the affective appraisal of a desktop virtual environment (VE) in simulated darkness. In the real world, darkness often evokes thoughts of vulnerability, threat, and danger, and may automatically precipitate emotional responses consonant with those thoughts (fear of darkness). This influences the affective appraisal of a given environment after dark and the way humans behave in that environment in conditions of low lighting. Desktop VEs are increasingly deployed to study the effects of environmental qualities and (architectural or lighting) interventions on human behaviour and feelings of safety. Their (ecological) validity for these purposes depends critically on their ability to correctly address the user’s cognitive and affective experience. Previous studies with desktop (i.e., non-immersive) VEs found that simulated darkness only slightly affects the user’s behavioral and emotional responses to the represented environment, in contrast to the responses observed for immersive VEs. We hypothesize that the desktop VE scenarios used in previous studies less effectively induced emotional and behavioral responses because they lacked personal relevance. In addition, factors like signs of social presence and relatively high levels of ambient lighting may also have limited these responses. In this study, young female volunteers explored either a daytime or a night-time (low ambient light level) version of a desktop VE representing a deserted (no social presence) prototypical Dutch polder landscape. To enhance the personal relevance of the simulation, a fraction of the participants were led to believe that the virtual exploration tour would prepare them for a follow-up tour through the real world counterpart of the VE. The affective appraisal of the VE and the emotional response of the participants were measured through self-report. The results show that the VE was appraised as slightly less pleasant and more arousing in simulated darkness (compared to a daylight) condition, as expected. However, the fictitious follow-up assignment had no emotional effects and did not influence the affective appraisal of the VE. Further research is required to establish the qualities that may enhance the validity of desktop VEs for both etiological (e.g., the effects of signs of darkness on navigation behaviour and fear of crime) and intervention (e.g., effects of street lighting on feelings of safety) research.

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The Effect of Searching Versus Surfing on Cognitive and Emotional Responses to Online News

Journal of Media Psychology Theories Methods and Applications ◽

10.1027/1864-1105.21.2.49 ◽

2009 ◽

Vol 21 (2) ◽

pp. 49-59 ◽

Cited By ~ 14

Author(s):

Kevin Wise ◽

Hyo Jung Kim ◽

Jeesum Kim

Keyword(s):

Heart Rate ◽

Skin Conductance ◽

Skin Conductance Level ◽

Emotional Responses ◽

Online News ◽

Self Report ◽

Mixed Design ◽

Information Need ◽

News Stories ◽

Rate Acceleration

A mixed-design experiment was conducted to explore differences between searching and surfing on cognitive and emotional responses to online news. Ninety-two participants read three unpleasant news stories from a website. Half of the participants acquired their stories by searching, meaning they had a previous information need in mind. The other half of the participants acquired their stories by surfing, with no previous information need in mind. Heart rate, skin conductance, and corrugator activation were collected as measures of resource allocation, motivational activation, and unpleasantness, respectively, while participants read each story. Self-report valence and recognition accuracy were also measured. Stories acquired by searching elicited greater heart rate acceleration, skin conductance level, and corrugator activation during reading. These stories were rated as more unpleasant, and their details were recognized more accurately than similar stories that were acquired by surfing. Implications of these results for understanding how people process online media are discussed.

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A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

10.26434/chemrxiv.7137827.v1 ◽

2018 ◽

Author(s):

Kyle Plunkett

Keyword(s):

Organic Chemistry ◽

Augmented Reality ◽

Real Time ◽

Real World ◽

Smart Phone ◽

Practical Method ◽

Virtual Expert ◽

Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

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A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽

10.3390/electronics10010013 ◽

2020 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Balaji M ◽

Chandrasekaran M ◽

Vaithiyanathan Dhandapani

Keyword(s):

Embedded Systems ◽

Embedded System ◽

Real Time ◽

Learning Outcomes ◽

Real World ◽

Time Constraints ◽

Network Simulator ◽

Practical Applications ◽

Rail Network ◽

Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

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Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

Journal of the American Academy of Audiology ◽

10.1055/s-0040-1719135 ◽

2020 ◽

Author(s):

Yu-Hsiang Wu ◽

Jingjing Xu ◽

Elizabeth Stangl ◽

Shareka Pentony ◽

Dhruv Vyas ◽

...

Keyword(s):

Real Time ◽

Self Report ◽

Data Logging ◽

Time Data ◽

Real Time Data ◽

Report Data ◽

Self Report Data ◽

Ecological Momentary ◽

The Impact ◽

Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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ViFin

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3448119 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-25

Author(s):

Wenqiang Chen ◽

Lin Chen ◽

Meiyi Ma ◽

Farshid Salemi Parizi ◽

Shwetak Patel ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Wearable Devices ◽

User Studies ◽

Finger Movement ◽

Writing System ◽

Time System ◽

Real Time System ◽

Input Devices ◽

A New Technique

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.

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OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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Assessing reinforcing versus aversive consequences in a real-time secondhand smoke intervention

Translational Behavioral Medicine ◽

10.1093/tbm/ibab004 ◽

2021 ◽

Author(s):

Vincent Berardi ◽

John Bellettiere ◽

Benjamin Nguyen ◽

Neil E Klepeis ◽

Suzanne C Hughes ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Secondhand Smoke ◽

Relative Effectiveness ◽

Feedback System ◽

Control Group ◽

Sensing Technology ◽

Control Versus ◽

Particle Counts ◽

Post Hoc

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

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Offline Algorithms in Low-Frequency Trading

Queue ◽

10.1145/3442632.3448307 ◽

2020 ◽

Vol 18 (6) ◽

pp. 37-51

Author(s):

Terence Kelly

Keyword(s):

Real Time ◽

Real World ◽

High Frequency ◽

Low Frequency ◽

Combinatorial Auctions ◽

Hard Problem ◽

Np Hard ◽

High Stakes ◽

Drill Bits ◽

Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

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Method for Viewing Real-World Scenes while Recording Video

Applied Sciences ◽

10.3390/app11104617 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4617

Author(s):

Daehee Park ◽

Cheoljun Lee

Keyword(s):

Real World ◽

Video Recording ◽

Emotional Responses ◽

Target Object ◽

Error Rates ◽

User Needs ◽

High Quality ◽

Gradient Algorithms ◽

Squared Error

Because smartphones support various functions, they are carried by users everywhere. Whenever a user believes that a moment is interesting, important, or meaningful to them, they can record a video to preserve such memories. The main problem with video recording an important moment is the fact that the user needs to look at the scene through the mobile phone screen rather than seeing the actual real-world event. This occurs owing to uncertainty the user might feel when recording the video. For example, the user might not be sure if the recording is of high-quality and might worry about missing the target object. To overcome this, we developed a new camera application that utilizes two main algorithms, the minimum output sum of squared error and the histograms of oriented gradient algorithms, to track the target object and recognize the direction of the user’s head. We assumed that the functions of the new camera application can solve the user’s anxiety while recording a video. To test the effectiveness of the proposed application, we conducted a case study and measured the emotional responses of users and the error rates based on a comparison with the use of a regular camera application. The results indicate that the new camera application induces greater feelings of pleasure, excitement, and independence than a regular camera application. Furthermore, it effectively reduces the error rates during video recording.

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