This project was funded, in part, by ConAgra Foods (Omaha, NE, USA), the Virginia Agricultural Experiment Station, the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Virginia Tech Water INTERface Interdisciplinary Graduate Education Program.

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The authors have nothing to disclose.

음식 및 음료에 관한 문헌에서 AFEA 응용 프로그램은 1-11 매우 제한되어있다. 음식이 응용 프로그램은 방법론과 데이터 해석을 설정하기위한 기회를 만드는 새로운 기능입니다. Arnade는 (2013) 7 초콜릿 우유와 곡선 분석 및 분산 분석에 따라 영역을 사용하여 흰 우유에 개인의 감정적 반응 중 높은 개인의 다양성을 발견했다. 슬프고 화나게 짧은 시간 응답 7 있었을 때 그러나...

자동 얼굴 표정 분석 (AFEA)는 음료 및 음식에 대한 감정적 인 반응을 특성화하기위한 전향 적 분석 도구입니다. 기존의 감각 과학 방법론, 식품 평가 사례 및 쾌락 규모의 평가에 여분의 차원을 추가 할 수 있습니다 감정적 인 분석은 일반적으로 연구 및 산업 설정에서 모두 사용했다. 감정 분석은 식품 및 음료에 대한 더 정확한 응답을 알 추가적인 측정을 제공 할 수있다. 반응 1을 기록 오류로 인해 쾌락 점수는 참가자 바이어스를 포함 할 수있다. AFEA 연구는 컴퓨터 게임, 사용자 행동, 교육 / 교육학과 공감과기만에 심리학 연구 등 많은 연구 응용 프로그램에서 사용되어왔다. 대부분의 식품 관련 연구는 식품의 품질과 음식 인간 행동에 대한 감정적 반응의 특성에 초점을 맞추고있다. 음식의 행동에 대한 통찰력을 얻고있는 최근의 추세, 문학 보고서의 성장 몸은 AFEA의 사용식품, 음료 및 냄새 물질 1-12과 관련된 인간의 감정적 반응의 특성을합니다. AFEA는 얼굴 액션 코딩 시스템 (FACS)에서 파생됩니다. 얼굴 동작 코딩 시스템 (FACS)를 5 점 강도 스케일 (13) 상에 작용 유닛 (AUS)을 특징으로 얼굴의 움직임을 구별한다. FACS 접근 방식은 훈련 된 리뷰 전문가, 수동 코딩, 중요한 평가 시간을 필요로하며, 제한된 데이터 분석 옵션을 제공합니다. AFEA 감정을 결정하기 위해 빠른 평가 방법으로 개발되었다. AFEA 소프트웨어는 감정적 반응 14-18의 특성을 얼굴 근육의 움직임, 얼굴 데이터베이스, 알고리즘에 의존합니다. 본 연구에 사용 된 AFEA 소프트웨어는 감정적 인 표정 사진의 바르샤바 세트 (WSEFEP)과 0.70의 표준 계약에 가까운 (ADFES) 설정 암스테르담 동적 얼굴 표정, 모두에서 평균 0.67 합의의 &quot;FACS 지수에 도달 수동 코딩 &quot;19 </sup&gt;. 분석에 포함 보편적 인 감정은 1-0의 별도의 규모 (각 행복 (​​긍정적), (음) 슬픔, 혐오 (음), 놀란 (양 또는 음), 화가 (음), 무서워 (음) 및 중립 0 발현되지 = 1 = 완전) 20 표명했다. 또한, 심리학 문헌은 무서워하고, (멀리 혐오 자극에서) 21 &quot;철수&quot;감정으로 혐오, 행복 놀라게하고 화가 &quot;접근&quot;감정 등 (자극 방향)과 슬픈이 포함되어 있습니다. 식품과 관련된 감정을 특성화에 대한 현재 AFEA 소프트웨어 중 하나 제한은 씹는과 삼키는뿐만 아니라 극단적 인 머리의 움직임과 같은 다른 총 모터 동작과 관련된 안면 운동의 간섭이다. 이 소프트웨어는 얼굴 16, 17에 500 개 이상의 근육 포인트를 기준으로 작은 얼굴 근육의 움직임, 관련 위치 및 운동의 정도를 목표로하고있다. 씹는 운동 식의 분류를 방해합니다. 이 제한ATION은 액상 식품을 사용하여 해결 될 수있다. 그러나, 다른 방법의 어려움은 또한 데이터 수집 환경 기술 연구원 설명서, 참가자 행동 및 참가자 속성을 포함한 비디오 감도 AFEA 분석을 감소시킬 수있다. 표준 방법론을 개발하고 최적의 비디오 캡처 및 관능 평가 실험실 환경에서 음식과 음료에 대한 감정적 반응에 대한 AFEA를 사용하여 데이터 분석을 위해 확인되지 않았습니다. 여러 측면 조명 참가자 방향 참가자 동작 참가자 높이이며, 또한, 카메라 높이 카메라, 낚시 및 장치 설정으로 인한 그림자 조명을 포함하는 비디오 캡쳐 환경에 영향을 미칠 수있다. 또한, 데이터 분석 방법론은 상충하는 감정적 반응을 평가하는 표준 방법이 부족하다. 여기, 우리는 (음료를 사용하여 의미있는 결과로 감정적 인 데이터 처리 데이터를 캡처에 대한 우리의 표준 운영 절차를 시연 할 예정이다맛 우유, 평가 향이 우유와 향이 물). 우리의 지식 하나의 피어 검토 출판에, 우리의 실험실 그룹에서, 감정 분석 8 데이터 해석에 대한 시계열을 이용하고있다; 그러나,이 방법은 우리의 표시 방법에 대해 업데이트되었습니다. 우리의 목표는 관능 평가 실험실 설정에서 재현성을 돕는 개선 된 일관된 방법을 개발하는 것이다. 데모를 들어, 연구 모델의 목적은 AFEA는 맛 우유, 향이 우유와 향이 물 기존의 쾌락 수용 평가를 보완 할 수 있는지 평가하는 것입니다. 동영상 프로토콜의 목적은, AFEA 방법론을 확립 도와 관능 평가 실험실에서 비디오 캡쳐 조건 (감각 부스 설정) 표준화, 및 모집단의 시간적 감정 데이터 분석을위한 방법을 도시한다.

<table><tbody><tr><td>2% Reduced Fat Milk</td><td>Kroger Brand, Cincinnati, OH or DZA Brands, LLC, Salisbury, NC</td><td>na</td><td>for solutions</td></tr><tr><td>Drinking Water</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>Imitation Clear Vanilla Flavor</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>Iodized Salt</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>FaceReader 6</td><td>Noldus Information Technology, Wageningen, The Netherlands</td><td>na</td><td>For Facial Analysis</td></tr><tr><td>Sensory Information Management System (SIMS) 2000</td><td>Sensory Computer Systems, Berkeley Heights, NJ</td><td>Version 6</td><td>For Sensory Data Capture</td></tr><tr><td>Rhapsody</td><td>Acuity Brands Lighting, Inc., Conyers, GA</td><td></td><td>For Environment Illumination</td></tr><tr><td>R Version&amp;nbsp;</td><td>R Core Team 2015</td><td>3.1.1</td><td>For Statistical Analysis</td></tr><tr><td>Microsoft Office</td><td>Microsoft</td><td>na</td><td>For Statistical Analysis</td></tr><tr><td>JMP</td><td>Statistical Analysis Software (SAS) Version 9.2, SAS Institute, Cary, NC</td><td>na</td><td>For Statistical Analysis</td></tr><tr><td>Media Recorder 2.5</td><td>Noldus Information Technology, Wageningen, The Netherlands</td><td>na</td><td>For capturing participants sensory evaluation</td></tr><tr><td>Axis M1054 Camera</td><td>Axis Communications, Lund, Sweden</td><td>na</td><td></td></tr><tr><td>Beverage</td><td></td><td>na</td><td>Beverage or soft food for evaluation</td></tr></tbody></table>

protocol for data collection and analysis applied to automated facial expression analysis technology and temporal analysis for sensory evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

이 방법은 AFEA 데이터 수집을위한 표준 프로토콜을 제안한다. 제안 된 프로토콜 단계를 따라하는 경우, 사용할 수없는 감정 데이터 출력 가난한 데이터 수집의 결과 (그림 1) (그림 2 :; 왼쪽 사진)은 제한 될 수 있습니다. 이 잘못된 데이터 (그림 1)와 같이 로그 파일 (.txt 인)은 주로 &quot;FIND_FAILED&quot; &quot;FIT_FAILED&quot;등을 포함하는 경우...

Watch this Scientific Journal Video about Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation at JoVE.com

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

자동화 표정 분석 소프트웨어를 사용하여 관능 평가 실험실에서 음료 및 액상 식품에 인구의 감정적 반응을 캡처 및 통계적 분석을위한 프로토콜을 설명한다.

관능 평가를위한 데이터 수집 및 자동화 표정 분석 기술에 대한 분석 응용 및 시간적 분석을위한 프로토콜

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

protocol-for-data-collection-analysis-applied-to-automated-facial

Universidad San Sebastian

Research

JoVE Journal

Behavior

9.3K 조회수.  Virginia Tech.  자동화 표정 분석 소프트웨어를 사용하여 관능 평가 실험실에서 음료 및 액상 식품에 인구의 감정적 반응을 캡처 및 통계적 분석을위한 프로토콜을 설명한다. 

비디오: Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)

Improved methods for assessing bulbar impairment are necessary for expediting diagnosis of bulbar dysfunction in ALS, for predicting disease progression across speech subsystems, and for addressing the critical need for sensitive outcome measures for ongoing experimental treatment trials. To address this need, we are obtaining longitudinal profiles of bulbar impairment in 100 individuals based on a comprehensive instrumentation-based assessment that yield objective measures. Using instrumental approaches to quantify speech-related behaviors is very important in a field that has primarily relied on subjective, auditory-perceptual forms of speech assessment1. Our assessment protocol measures performance across all of the speech subsystems, which include respiratory, phonatory (laryngeal), resonatory (velopharyngeal), and articulatory. The articulatory subsystem is divided into the facial components (jaw and lip), and the tongue. Prior research has suggested that each speech subsystem responds differently to neurological diseases such as ALS. The current protocol is designed to test the performance of each speech subsystem as independently from other subsystems as possible. The speech subsystems are evaluated in the context of more global changes to speech performance. These speech system level variables include speaking rate and intelligibility of speech.
The protocol requires specialized instrumentation, and commercial and custom software. The respiratory, phonatory, and resonatory subsystems are evaluated using pressure-flow (aerodynamic) and acoustic methods. The articulatory subsystem is assessed using 3D motion tracking techniques. The objective measures that are used to quantify bulbar impairment have been well established in the speech literature and show sensitivity to changes in bulbar function with disease progression. The result of the assessment is a comprehensive, across-subsystem performance profile for each participant. The profile, when compared to the same measures obtained from healthy controls, is used for diagnostic purposes. Currently, we are testing the sensitivity and specificity of these measures for diagnosis of ALS and for predicting the rate of disease progression. In the long term, the more refined endophenotype of bulbar ALS derived from this work is expected to strengthen future efforts to identify the genetic loci of ALS and improve diagnostic and treatment specificity of the disease as a whole. The objective assessment that is demonstrated in this video may be used to assess a broad range of speech motor impairments, including those related to stroke, traumatic brain injury, multiple sclerosis, and Parkinson disease.

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Objective assessments of the physiological mechanisms that support speech are needed to monitor disease onset and progression in persons with ALS and to quantify treatment effects in clinical trials. In this video, we present a comprehensive, instrumentation-based protocol for quantifying speech motor performance in clinical populations.

루게릭병에 안구 장애의 종합 평가 (ALS)에 대한 프로토콜

Improved methods for assessing bulbar impairment are necessary for expediting diagnosis of bulbar dysfunction in ALS, for predicting disease progression ...

연구

의학

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III).
The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called &#34;stimulus mouse&#34;).
In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse.
The automated evaluation of the movie detects the nose of the test mouse, which allows the determination of all described sociability measures in session I and II (in session III, approaches are identified automatically but classified manually). To find the nose, the image of an empty cage is digitally subtracted from each frame of the movie and the resulting image is binarized to identify the mouse pixels. The mouse tail is automatically removed and the two most distant points of the remaining mouse are determined; these are close to nose and base of tail. By analyzing the motion of the mouse and using continuity arguments, the nose is identified.
<img alt="Figure 1" src="/files/ftp_upload/52508/52508fig1.jpg" /> 
Figure 1. Assessment of Sociability During 3 sessions. Session I (top): Acclimation of test mouse to the cage. Session II (middle): Test mouse moving freely in the cage while the stimulus mouse is enclosed in an inverted cup. Session III (bottom): Both test mouse and stimulus mouse are allowed to move freely and interact with each other.

This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.

자동화 된 이미지 처리 방식을 사용하여 마우스 사교성의 실험 평가

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the ...

행동분석학

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

참여의 여러 차원을 평가하는 것은 학습의 특성을 : 신경 생리학 관점

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion expressions, and multimodal (i.e., film clips) stimuli that included contextual cues. All stimuli were presented via computer. Participants were required to indicate how the person in each stimulus was feeling using a forced-choice format. Additionally, for the film clips, participants had to indicate how they felt in response to the stimulus, and the level of intensity with which they experienced that emotion.

This paper describes how to implement a battery of behavioral tasks to examine emotion recognition of isolated facial and vocal emotion expressions, and a novel task using commercial television and film clips to assess multimodal emotion recognition that includes contextual cues.

외상성 뇌 손상을 가진 사람들에 의해 감정 인식을 평가하기 위해 격리 식 및 필름 클립의 사용을 탐구

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion ...

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and superior colliculus especially with fearful faces1-3. These regions constitute the limbic structure in non-conscious perception of emotions and modulate cortical activity either directly or indirectly2. In contrast, the conscious representation of emotions is more pronounced in the anterior cingulate, prefrontal cortex, and somatosensory cortex for directing voluntary attention to details in faces3,4. Asperger&#39;s syndrome (AS)5,6 represents an atypical mental disturbance that affects sensory, affective and communicative abilities, without interfering with normal linguistic skills and intellectual ability. Several studies have found that functional deficits in the neural circuitry important for facial emotion recognition can partly explain social communication failure in patients with AS7-9. In order to clarify the interplay between conscious and non-conscious representations of emotional faces in AS, an EEG experimental protocol is designed with two tasks involving emotionality evaluation of either photograph or line-drawing faces. A pilot study is introduced for selecting face stimuli that minimize the differences in reaction times and scores assigned to facial emotions between the pretested patients with AS and IQ/gender-matched healthy controls. Information from the pretested patients was used to develop the scoring system used for the emotionality evaluation. Research into facial emotions and visual stimuli with different spatial frequency contents has reached discrepant findings depending on the demographic characteristics of participants and task demands2. The experimental protocol is intended to clarify deficits in patients with AS in processing emotional faces when compared with healthy controls by controlling for factors unrelated to recognition of facial emotions, such as task difficulty, IQ and gender.

An EEG experimental protocol is designed to clarify the interplay between conscious and non-conscious representations of emotional faces in patients with Asperger&#39;s syndrome. The technique suggests that patients with Asperger&#39;s syndrome have deficits in non-conscious representation of emotional faces, but have comparable performance in conscious representation with healthy controls.

아스퍼거 증후군의 감정적 인 얼굴의 의식과 비 의식 표현

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and ...

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. Touch that optimally activates CT fibers is consistently rated as hedonically pleasant. Patient groups with impaired social-emotional functioning also show disordered affective touch ratings. However, relying on self-reported ratings of touch has many limitations, including recall bias and communication barriers. Here, we describe a methodological approach to study affective responses to touch via facial electromyography (EMG) that circumvents the reliance on self-report ratings. Facial EMG is an objective, quantitative, and non-invasive method to measure facial muscle activity indicative of affective responses. Responses can be assessed across healthy and patient populations without the need for verbal communication. Here, we provide two separate datasets demonstrating that CT-optimal and non-optimal touch elicit distinct facial muscle reactions. Moreover, facial EMG responses are consistent across stimulus modalities, e.g. tactile (experienced touch) and visual (observed touch). Finally, the temporal resolution of facial EMG can detect responses on timescales that supersede that of verbal reporting. Together, our data suggest that facial EMG is a suitable methodology for use in affective tactile research that can be used to supplement, or in some cases, supplant, existing measures.

We describe a protocol to assess facial muscle activity in response to experienced and observed tactile stimulation using facial electromyography.

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

파킨슨 병의 운동 및 인지 기능의 동적 디지털 바이오 마커

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

학습 프로세스에 종사하는 성인의 행동 분석에 사용되는 눈 추적 기술 및 데이터 마이닝 기술

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

교실 참여 향상: 학생 주의 수준에 대한 AI 기반 분석

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

저자 스포트라이트: 교실 주의력 측정의 기술적 및 주관적 문제 해결

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

학생의 주의력 수준을 감지하기 위한 인공 지능 기반 시스템

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...

관능 평가를위한 데이터 수집 및 자동화 표정 분석 기술에 대한 분석 응용 및 시간적 분석을위한 프로토콜

요약

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이 비디오의 챕터

루게릭병에 안구 장애의 종합 평가 (ALS)에 대한 프로토콜

자동화 된 이미지 처리 방식을 사용하여 마우스 사교성의 실험 평가

참여의 여러 차원을 평가하는 것은 학습의 특성을 : 신경 생리학 관점

외상성 뇌 손상을 가진 사람들에 의해 감정 인식을 평가하기 위해 격리 식 및 필름 클립의 사용을 탐구

아스퍼거 증후군의 감정적 인 얼굴의 의식과 비 의식 표현

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

파킨슨 병의 운동 및 인지 기능의 동적 디지털 바이오 마커

학습 프로세스에 종사하는 성인의 행동 분석에 사용되는 눈 추적 기술 및 데이터 마이닝 기술

학생의 주의력 수준을 감지하기 위한 인공 지능 기반 시스템

학생의 주의력 수준을 감지하기 위한 인공 지능 기반 시스템