Fun in Fusion Research (Elsevier Insights)

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The data were collected from different devices, e. The results and discussion of evaluations are presented further below. The accuracy of the data acquisition and synchronization process was validated by connected different devices, such as EEG, camera, second-generation Kinect, eye-tracker, and PC with the Lean UX platform cloud.

All data streams from multimodal data sources were acquired, synchronized at server endpoints, and checked for data accuracy using a three-second window size. The rate of missing data packets was used to measure the accuracy of data acquisition module shown in Table 2. The results show a 0. The results are shown in Figure The multimodal data from all devices were effectively synchronized at cloud endpoints in milliseconds. The synchronization module synchronized all sensors, stimuli and API data streams in real-time without manual post-synchronization of data.

For example, the eye-tracker, Kinect, camera, microphone, EEG, and interaction tracker communicated at ms, ms, ms, ms, ms, and ms, respectively, at the first window frame. The synchronization module recognized that all incoming data streams belonged to the single event.

The results depicted that all data streams were well synchronized in a real-time manner, showing the perfection of the synchronization module. Table 3 shows the confusion matrix of automatic facial expressions for Cohn-Kanade dataset. Figure 14 shows the average accuracy for each dataset. The results show a high accuracy for the happy, anger, sadness and surprise, while relatively low accuracy for the fear and disgust. However, generally, the model accuracy was quite reasonable compared to the other video-based emotion recognizers.

There were some challenges for effective emotion recognition for heterogeneous populations with respect to demographic, cultural, and impairment aspects, which can be resolved by improving the landmarking techniques to classify the emotions for face impairment. Audio-based emotion recognition : The result of audio-based emotion metric extraction is shown in Table 4 for Emo-DB dataset. The results show a high accuracy for anger and surprise, while a relatively low accuracy is shown for happy and disgust. Additionally, happy and anger were mixed owing to the high sound pitch, while sadness and neutral were mixed owing to the soft voice.

However, generally, the model accuracy was quite reasonable compared to the other audio-based emotion recognizers. There were some challenges, such as tone differences and voice pitch, which made the audio-based emotion recognition difficult. The results are shown in Figure 15 , where DE achieves a higher accuracy for all frequency bands compared to the other features.

From the experiment and results, we identified that the DE feature is more suitable to fuse with other features of emotion recognizers, such pupil size of the eye tracking data. Average accuracy of the classifier using different features on different frequency bands. Pupil Diameter : We performed different experiments based on the pupil size metric using an eye tracker to observe how the pupil size changed in accordance with different emotional states. From the experiments, we found that the pupil size increased dilated in a sorrowful state, and was smallest in a calm state, as shown in Figure For both positive and negative emotions, the pupil size was larger compared with neutral, which showed a correlation with different emotions.

Emotion fusion : Table 5 shows the average accuracy of emotion fusion. The results show that fusion accuracy is higher than the individual classifier accuracy. The t -test analysis showed no significant differences between the feature level and decision level fusion. The open-ended question analyzer module assessed the affective content sentiment and emotions by using lexicon-based dictionaries; POS-tagging; bag-of-words; and in combination with classifiers, such as SVM or NB.

We used multiple lexicon dictionaries e. For feature selection, filter and wrapper approaches were used for the selection of optimal features that improved the classification accuracy. For the experiment, we used five datasets that are widely used for text-based sentiment analysis.

The results of experiments shown in Table 6 reveal that the ensemble method with minimal feature selection strategies can effectively increase the accuracy of classification compared with the baseline classifier. Understanding user feelings, thoughts, and needs are very important to engaging, sustaining, and increasing the purchase of a product, system, or service.

The UX assessment reveals the user feeling about the product, system, or service and their functionalities. The user may have difficulty expressing their feelings and thoughts about a product, system, or service through traditional methods. Sometimes they may be unable to interpret their own feelings in order to describe them. The physiological measurements in assessing UX can detect emotional arousal and stress, motivation, and visual attention that have direct relationships with user cognitive and affective states in a non-intrusive way.

The mixed-method approach showed importance in the UX evaluation methods by providing more accurate and precise information about the user while interacting with the product.

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However, this approach requires a skilled researcher to integrate multiple devices, synchronize data, analyze data, and make informed decisions. Thus, we developed the Lean UX platform to provide an integrated environment in a seamless manner with real-time synchronization and powerful visualizations. The platform offers plug-and-play support for data collection from different devices and powerful real-time analytics visualization to enable insights of time spans of the user experience with multiple participants.

Further, it helps identify the areas of improvement after assessment of any product, system, or service to improve the overall UX. However, improvements can be made in terms of the classifier performance. Finally, we will increase the datasets for effective emotional state recognition. Table A1 shows the selected items from existing UX questionnaires.

Appendix B depicts the lean UX platform toolkit. The input modalities are dependent on the UX evaluation type. In the survey, we are using User Experience Questionnaire UEQ scale to collect the user experience for measuring the UX, contains six dimension scales such as novelty, stimulation, attractiveness, dependability, and efficiency. While for the other type of UX evaluation, all types of input modalities will be available. The moderator can select any type of input modalities, depend on their study.

After successful creation of the project, the moderator can add tasks to project as shown in Figure A2 and Figure A3. The system will generate automatically the project Id, which is used by the interaction tracker module, to track the user interaction as discussed in Section 4. The moderator first adds the JavaScript code in the header of each page by assigning the project id. The JavaScript code is also responsible to display the feedback form on the completion of the task or error situation. The moderator can collect the UX measurement data by connected the sensors, sensors connectivity is auto checked by the system.

The moderator should add the participant information by adding their name, age, and gender. The moderator can check the different modalities measures such as automatic facial expression analysis, emo voice, interaction tracker analytics e. The participants can express their feeling in both Likert scale and free text format. The self-reported data will be available on the submission of self-reported form by the participant to the moderator.

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The open end question analyzer will analyze the free text self-reported feedback to extract the user sentiment and emotions related to that task shown in Figure A4. This evaluation process will repeat for all participants who will participate in the study for each task. The moderator can check the results of UX evaluation at the task level and project level as shown in Figure A5 and Figure A6. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online May Find articles by Jamil Hussain. Find articles by Wajahat Ali Khan. Find articles by Taeho Hur.

Find articles by Hafiz Syed Muhammad Bilal. Find articles by Jaehun Bang. Find articles by Anees Ul Hassan. Find articles by Sungyoung Lee.

Author information Article notes Copyright and License information Disclaimer. Received Mar 16; Accepted May Abstract The user experience UX is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners.

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Keywords: user experience evaluation, user experience measurement, eye-tracking, facial expression, galvanic skin response, EEG, interaction tracker, self-reporting, user experience platform, mix-method approach. Introduction The user experience UX is a multi-faceted research area that includes diverse aspects of the experiential and affective use of a product, system, or service [ 1 , 2 ].

Related Work Many approaches have been proposed to acquire the user experience in various ways, including the questionnaire, facial analysis, vocal analysis, biometrics, and others. Observational Measurement Observational measurement is an alternative approach to self-reporting or other methods of measuring user behavior.

Physiological Measurement In this section, we explore different biometric sensors, which obtain physical information as quantifiable data for the UX assessment. Open in a separate window. Figure 1. Figure 2. Figure 3. Figure 4. User Interaction Metrics This module handles the collection of the user interactions and calculating the system performance. We use multimodal data from various sensors, such as eye tracking, for visual attention and EEG for quick detection of emotions, motivations, engagement arousal in the cognitive workload and frustration level.

In this study, we implemented the eye tracking and EEG modules. Automatic facial expression analysis AFEA plays an important role in producing deeper insights in human emotional reactions valence , such as fear, happiness, sadness, surprise, anger, disgust, or neutrality. For AFEA, we used an inexpensive webcam to capture video of a participant in order to reduce the overall financial cost. Our developed AFEA first detects the face in a given video frame or image by applying the Viola Jones cascaded classifier algorithm.

Second, different facial landmarks features are detected e. Finally, the face model is fed into the classifier to provide emotions and facial expression metrics as labels [ 41 ]. Non-verbal gestures i. We will use a depth camera to recognize emotions through user body language in upcoming version of lean UX platform release. The model classifies incoming audio to the platform as seven basic emotions: fear, happiness, sadness, surprise, anger, disgust, or neutrality. The speech signals are divided into frames, then STE detects the energy within each frame for voice segmentation.

Afterward, STZCR is calculated from the rate of change of speech signal within a particular time window. These two features are used to extract the speech segment for emotion recognition and removed the unwanted frames from signals. Subsequently, we have employed the feature level fusion using a set of rules to choose the right emotions as a previous study [ 75 ]. It shows the importance of making a multimodal fusion framework that could effectively extract emotions from different modalities in human-centric environment. The benefit of using multimodal data from different devices is to get deep insights of human emotions and motivations.

Figure 5. Self-Reported Metrics Self-reported metrics [ 35 ] deal with post-tasks that explicitly ask questions about the participant for information about their opinion and their interaction with the system, for example, overall interaction, ease of use, satisfaction, effectiveness, and efficacy. Table 1 A partial list of candidate rules.

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Figure 6. Figure 7. Survey workflow: triangulation of UX metric with self-reporting. Figure 8. Figure 9. Analytics Layer The analytics layer is responsible for providing different analytics based on the UX expert query. Visualization Server UX Toolkit The visualization server is a client application that is used by the UX expert to evaluate the product, system, or service.

Figure Results and Evaluation The proposed platform was evaluated from different aspects, such as multimodal data acquisition error rate, synchronization accuracy, individual UX measurements metrics ranging from interactions, multimodal emotions recognizers, and self-reported assessments.

Multimodal Data Acquisition and Data Synchronization Process The accuracy of the data acquisition and synchronization process was validated by connected different devices, such as EEG, camera, second-generation Kinect, eye-tracker, and PC with the Lean UX platform cloud. Table 2 The data acquisition process accuracy. Table 5 The average accuracies of each classifier and fusion method. Subject Facial Expression Audio Base Textual EEG DE Eye Tracking Fusion Feature Level Decision Level 1 95 84 91 68 80 96 96 2 92 82 89 63 82 97 98 3 80 94 64 83 98 99 4 98 83 89 62 89 93 98 5 98 84 93 76 90 92 93 6 90 83 94 70 81 97 98 7 94 84 94 72 87 91 93 8 93 83 91 69 85 94 94 9 93 80 92 64 80 95 93 10 98 82 92 70 87 98 96 Average Self-Reported Metric The open-ended question analyzer module assessed the affective content sentiment and emotions by using lexicon-based dictionaries; POS-tagging; bag-of-words; and in combination with classifiers, such as SVM or NB.

Table 6 Average accuracies of each classifier for each dataset. Conclusions Understanding user feelings, thoughts, and needs are very important to engaging, sustaining, and increasing the purchase of a product, system, or service. Table A1 The average accuracies of each classifier and fusion method.

Question ID Bipolar Words WL WR 1 annoying enjoyable 2 not understandable understandable 3 dull Creative 4 difficult to learn easy to learn 5 inferior valuable 6 boring exciting 7 not interesting interesting 8 unpredictable predictable 9 slow fast 10 inventive conventional 11 obstructive supportive 12 bad good 13 complicated easy 14 unlikable pleasing 15 usual leading edge 16 unpleasant pleasant 17 not secure secure 18 motivating demotivating 19 Does not meets expectations meet expectations 20 inefficient effient 21 confusing clear 22 impractical practical 23 cluttered organized 24 unattractive attractive 25 unfriendly friendly 26 conservative innovative 27 technical human 28 isolating connective 29 unprofessional professional 30 cheap premium 31 alienating integrating 32 separates me brings me closer 33 unpresentable presentable 34 cautious bold 35 undemanding challenging 36 ordinary novel 37 rejecting inviting 38 repelling appealing 39 disagreeable likeable.

Figure A1. Figure A2. Figure A3. Figure A4. Momentary UX evaluation: real-time data collection and UX metric measurement. Figure A5. Figure A6. Author Contributions J. Conflicts of Interest The authors declare no conflict of interest. References 1. Hassenzahl M. User experience—A research agenda. Liang Y. Sungkyunkwan University; Seoul, Korea: Kula I. Law E.

Roto V. User experience white paper. Bringing clarity to the concept of user experience.

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Goodman E. IEEE Trans. Kuniavsky M. Eye tracking the user experience—An evaluation of ontology visualization techniques. Web J. Springer; Cham, Switzerland: Bojko A. Zheng W. Identifying stable patterns over time for emotion recognition from EEG. ITM Web of Conferences. Volume Liu Y. Let us wish you a happy birthday! Date of Birth. Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Month January February March April May June July August September October November December Year Please fill in a complete birthday Enter a valid birthday.

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