An Augmented Affective-Cognition Framework for Usability Studies of In-Vehicle System User Interface

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
User Interface ◽  
Real Time ◽  
Coherent System ◽  
Emotional States ◽  
Subjective Data ◽  
Cognitive States ◽  
Facial Emg ◽  
Vehicle System ◽  
System User ◽  
Set Up

Vehicles with better usability have become increasingly popular due to their ease of operations and safety for driving. However, the way how usability of in-vehicle system user interface is studied still needs improvement. This paper concerns how to use advanced computational, neurophysiology- and psychology-based tools and methodologies to determine affective (emotional) states and behavioral data of an individual in real time and in turn how to adapt the human-vehicle interaction to meet the user’s cognitive needs based on this real-time assessment. Specifically, we set up a set of neuro-physiological equipment that is capable of collecting EEG, facial EMG (electromyography), skin conductance response, and respiration data and a set of motion sensing and tracking equipment that is capable of eye ball movement and objects that the user interacts. All hardware components and software is integrated into a cohesive augmented sensor platform that can perform as “one coherent system” to enable multi-modal data processing and information inference for context-aware analysis of affective and cognitive states based on the rough set inference engine. Meanwhile subjective data is also recorded for comparison. A usability study of in-vehicle system UI is shown to demonstrate the potential of the proposed methodology.

Augmented Affective-Cognition for Usability Study of In-Vehicle System User Interface

2014 ◽  
Vol 14 (2) ◽  
Author(s):  
Feng Zhou ◽  
Yangjian Ji ◽  
Roger J. Jiao
Keyword(s):  
User Interface ◽  
Real Time ◽  
Coherent System ◽  
Emotional States ◽  
Usability Study ◽  
Subjective Data ◽  
Vehicle System ◽  
Vehicle Systems ◽  
Behavior Based ◽  
And Behavior

Usability of in-vehicle systems has become increasingly important for ease of operations and safety of driving. The user interface (UI) of in-vehicle systems is a critical focus of usability study. This paper studies how to use advanced computational, physiology- and behavior-based tools and methodologies to determine affective/emotional states and behavior of an individual in real time and in turn how to adapt the human-vehicle interaction to meet users' cognitive needs based on the real-time assessment. Specifically, we set up a set of physiological sensors that are capable of collecting EEG, facial EMG, skin conductance response, and respiration data and a set of motion sensing and tracking equipment that is capable of capturing eye ball movement and objects which the user is interacting with. All hardware components and software are integrated into an augmented sensor platform that can perform as “one coherent system” to enable multimodal data processing and information inference for context-aware analysis of emotional states and cognitive behavior based on the rough set inference engine. Meanwhile subjective data are also recorded for comparison. A usability study of in-vehicle system UI is shown to demonstrate the potential of the proposed methodology.

A “Visible-and-Controllable” Smart Home System

2014 ◽  
Vol 1046 ◽  
pp. 492-495 ◽  
Author(s):  
Ran Zhang ◽  
Jun Meng
Keyword(s):  
User Interface ◽  
Real Time ◽  
User Experience ◽  
Smart Home ◽  
The Real ◽  
Real Time Image ◽  
System User ◽  
Smart Appliance ◽  
Time Image

This paper introduces a smart home system with new user experience. The system uses the mobile smart terminal to recognize the smart appliance based on the real-time image of the smart appliance captured by the terminal, then the terminal generates a user-interface (UI) for the user to control the smart appliance recognized. Using this system, user no longer needs to search the smart appliance that he wants to control in the complicated menu, instead, he just use the camera to capture the image of the appliance, then he will gets the controlling UI of the appliance; it means that as long as you can see the appliance, then you can control it, and that is the “Visible-and-Controllable” effect.

Recent Developments of Ambulatory Assessment Methods

2014 ◽  
Vol 25 (4) ◽  
pp. 279-287 ◽  
Author(s):  
Stefan Hey ◽  
Panagiota Anastasopoulou ◽  
André Bideaux ◽  
Wilhelm Stork
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
New Technologies ◽  
Psychological Research ◽  
Ambulatory Assessment ◽  
Emotional States ◽  
Recent Developments ◽  
Potential Applications ◽  
Information And Communication ◽  
Physiological Sensors

Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.

DEVELOPMENT OF MULTIPLEX REAL-TIME PCR ASSAY FOR SIX PATHOGENS: RAPID TOOL FOR DIAGNOSIS OF BACTERIAL MENINGITIS

2019 ◽  
pp. 60-66
Author(s):  
Viet Quynh Tram Ngo ◽  
Thi Ti Na Nguyen ◽  
Hoang Bach Nguyen ◽  
Thi Tuyet Ngoc Tran ◽  
Thi Nam Lien Nguyen ◽  
...  
Keyword(s):  
Bacterial Meningitis ◽  
Real Time ◽  
Real Time Pcr ◽  
Diagnostic Methods ◽  
Type B ◽  
E Coli ◽  
Pcr Assays ◽  
Set Up ◽  
Etiological Agents ◽  
Suis Serotype

Introduction: Bacterial meningitis is an acute central nervous infection with high mortality or permanent neurological sequelae if remained undiagnosed. However, traditional diagnostic methods for bacterial meningitis pose challenge in prompt and precise identification of causative agents. Aims: The present study will therefore aim to set up in-house PCR assays for diagnosis of six pathogens causing the disease including H. influenzae type b, S. pneumoniae, N. meningitidis, S. suis serotype 2, E. coli and S. aureus. Methods: inhouse PCR assays for detecting six above-mentioned bacteria were optimized after specific pairs of primers and probes collected from the reliable literature resources and then were performed for cerebrospinal fluid (CSF) samples from patients with suspected meningitis in Hue Hospitals. Results: The set of four PCR assays was developed including a multiplex real-time PCR for S. suis serotype 2, H. influenzae type b and N. meningitides; three monoplex real-time PCRs for E. coli, S. aureus and S. pneumoniae. Application of the in-house PCRs for 116 CSF samples, the results indicated that 48 (39.7%) cases were positive with S. suis serotype 2; one case was positive with H. influenzae type b; 4 cases were positive with E. coli; pneumococcal meningitis were 19 (16.4%) cases, meningitis with S. aureus and N. meningitidis were not observed in any CSF samples in this study. Conclusion: our in-house real-time PCR assays are rapid, sensitive and specific tools for routine diagnosis to detect six mentioned above meningitis etiological agents. Key words: Bacterial meningitis, etiological agents, multiplex real-time PCR

The idiodynamic method: willingness to communicate and anxiety processes interacting in real time

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Peter D. MacIntyre ◽  
Tammy Gregersen
Keyword(s):  
Real Time ◽  
Complex Dynamics ◽  
Video Recording ◽  
Research Participant ◽  
Willingness To Communicate ◽  
Human Communication ◽  
Cognitive States ◽  
Communication Anxiety ◽  
Observer Ratings ◽  
Teacher Empathy

Abstract The idiodynamic method is a relatively new mixed-method approach to studying in real time the complex dynamics of integrated affective and cognitive states that interact continuously with human communication. The method requires video recording a sample of communication from a research participant and then using specialized software to play the video back while collecting contemporaneous self-reported ratings (approximately one per second) on one or more focal variables of interest to the researcher, such as willingness to communicate (WTC) or communication anxiety (CA). After the participant rates the communication sample, a continuous graph of changes in the focal variable is printed. The final step is to interview the speaker to gather an explanation for changes in the ratings, for example at peaks or valleys in the graph. The method can also collect observer ratings that can then be compared with the speaker’s self-ratings. To date, studies have been conducted examining WTC, CA, motivation, perceived competence, teacher self-efficacy, teacher empathy, and strategy use, among other topics. The strengths and limitations of the method will be discussed and a specific example of its use in measuring WTC and CA will be provided.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

scholarly journals Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Yixiang Lim ◽  
Nichakorn Pongsarkornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Trevor Kistan ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Simulation Environment ◽  
Adaptive Automation ◽  
Cognitive States ◽  
Aerial Vehicles ◽  
Machine Interface ◽  
Multiple Unmanned Aerial Vehicles ◽  
Online Adaptation ◽  
Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

scholarly journals The impact of translation modality on user experience: an eye-tracking study of the Microsoft Word user interface

2021 ◽  
Author(s):  
Ana Guerberof Arenas ◽  
Joss Moorkens ◽  
Sharon O’Brien
Keyword(s):  
User Interface ◽  
Eye Tracking ◽  
Cognitive Load ◽  
Task Completion ◽  
Eye Tracker ◽  
Software Products ◽  
Microsoft Word ◽  
Set Up ◽  
The Impact ◽  
The Given

AbstractThis paper presents results of the effect of different translation modalities on users when working with the Microsoft Word user interface. An experimental study was set up with 84 Japanese, German, Spanish, and English native speakers working with Microsoft Word in three modalities: the published translated version, a machine translated (MT) version (with unedited MT strings incorporated into the MS Word interface) and the published English version. An eye-tracker measured the cognitive load and usability according to the ISO/TR 16982 guidelines: i.e., effectiveness, efficiency, and satisfaction followed by retrospective think-aloud protocol. The results show that the users’ effectiveness (number of tasks completed) does not significantly differ due to the translation modality. However, their efficiency (time for task completion) and self-reported satisfaction are significantly higher when working with the released product as opposed to the unedited MT version, especially when participants are less experienced. The eye-tracking results show that users experience a higher cognitive load when working with MT and with the human-translated versions as opposed to the English original. The results suggest that language and translation modality play a significant role in the usability of software products whether users complete the given tasks or not and even if they are unaware that MT was used to translate the interface.

scholarly journals An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development

2021 ◽  
Vol 11 (16) ◽  
pp. 7197
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Shan Bao
Keyword(s):  
User Interface ◽  
Real Time ◽  
Interface Design ◽  
Situational Awareness ◽  
Warning System ◽  
User Interface Design ◽  
Warning Systems ◽  
Projection Algorithms ◽  
Time Projection ◽  
Timely Warning

Warning pedestrians of oncoming vehicles is critical to improving pedestrian safety. Due to the limitations of a pedestrian’s carrying capacity, it is crucial to find an effective solution to provide warnings to pedestrians in real-time. Limited numbers of studies focused on warning pedestrians of oncoming vehicles. Few studies focused on developing visual warning systems for pedestrians through wearable devices. In this study, various real-time projection algorithms were developed to provide accurate warning information in a timely way. A pilot study was completed to test the algorithm and the user interface design. The projection algorithms can update the warning information and correctly fit it into an easy-to-understand interface. By using this system, timely warning information can be sent to those pedestrians who have lower situational awareness or obstructed view to protect them from potential collisions. It can work well when the sightline is blocked by obstructions.

scholarly journals Operational Research to Assess the Real-Time Impact of COVID-19 on TB and HIV Services: The Experience and Response from Health Facilities in Harare, Zimbabwe

2021 ◽  
Vol 6 (2) ◽  
pp. 94
Author(s):  
Pruthu Thekkur ◽  
Kudakwashe C. Takarinda ◽  
Collins Timire ◽  
Charles Sandy ◽  
Tsitsi Apollo ◽  
...  
Keyword(s):  
Hiv Testing ◽  
Treatment Outcomes ◽  
Real Time ◽  
Treatment Success ◽  
Health Facilities ◽  
Case Detection ◽  
Monthly Data ◽  
Hiv Services ◽  
Tb Treatment ◽  
Set Up

When COVID-19 was declared a pandemic, there was concern that TB and HIV services in Zimbabwe would be severely affected. We set up real-time monthly surveillance of TB and HIV activities in 10 health facilities in Harare to capture trends in TB case detection, TB treatment outcomes and HIV testing and use these data to facilitate corrective action. Aggregate data were collected monthly during the COVID-19 period (March 2020–February 2021) using EpiCollect5 and compared with monthly data extracted for the pre-COVID-19 period (March 2019–February 2020). Monthly reports were sent to program directors. During the COVID-19 period, there was a decrease in persons with presumptive pulmonary TB (40.6%), in patients registered for TB treatment (33.7%) and in individuals tested for HIV (62.8%). The HIV testing decline improved in the second 6 months of the COVID-19 period. However, TB case finding deteriorated further, associated with expiry of diagnostic reagents. During the COVID-19 period, TB treatment success decreased from 80.9 to 69.3%, and referral of HIV-positive persons to antiretroviral therapy decreased from 95.7 to 91.7%. Declining trends in TB and HIV case detection and TB treatment outcomes were not fully redressed despite real-time monthly surveillance. More support is needed to transform this useful information into action.

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