Name: design and evaluation of smart glasses for food intake and physical activity classification
Uploaded: 2018-02-14T13:00:00.000+00:00
Duration: 7 min 47 s
Description: Watch this Scientific Journal Video about Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification at JoVE.com

עבודה זו נתמכה על ידי Envisible, inc. מחקר זה גם נתמך על ידי מענק של קוריאנית בריאות טכנולוגיית R & D הפרוייקט, משרד הבריאות & רווחה, הרפובליקה של קוריאה (HI15C1027). מחקר זה גם נתמך על ידי נבחרת מחקר קרן של קוריאה (ה-NRF-2016R1A1A1A05005348).

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המחברים אין לחשוף.

במחקר זה, אנחנו הציע לראשונה את העיצוב ואת תהליך הייצור של המשקפיים, לחוש את דפוסי צריכת המזון ופעילות גופנית. כמו במחקר זה התמקדו בעיקר ניתוח הנתונים כדי להבחין בין צריכת המזון מפעילויות גופניות אחרות (כגון הליכה, קריצות), מערכת רכישה חיישן ונתונים נדרש יישום ניידות הקלטה. לפיכך, המערכת כל...

רציף ואובייקטיבי ניטור צריכת המזון הוא חיוני לשמירה על איזון האנרגיה בגוף האדם, כמו הצטברות עודף אנרגיה עלול לגרום overweightness והשמנה1, אשר יכול לגרום לסיבוכים רפואיים שונים2. הגורמים העיקריים בחוסר איזון אנרגיה ידועים להיות צריכת המזון מופרז וגם לא מספיק פעילות גופנית3. מחקרים שונים על הפיקוח על ההוצאה האנרגטית היומית הוכנסו עם מדידת אוטומטי ואובייקטיבי דפוסי פעילות גופנית דרך מכשירים שכאלו4,5,6, גם צרכן הקצה ברמה ורפואי שלב7. מחקר על הפיקוח על צריכת המזון, אולם הוא עדיין באווירה מעבדה, מכיוון שזה קשה לזהות את הפעילות צריכת מזון באופן ישיר ואובייקטיבי. כאן, אנו שואפים להציג עיצוב המכשיר ואת ההערכה שלו עבור ניטור צריכת המזון ודפוסי פעילות גופנית ברמה המעשית בחיי היומיום.
היו מגוון גישות עקיף כדי לפקח על צריכת המזון דרך הלעיסה, הבליעה נשמע8,9,10, תנועת היד11,12,13, תמונה ניתוח14ו- electromyogram (EMG)15. עם זאת, גישות אלה היו קשים להחיל על יישומים חיי היומיום, בשל מגבלות הטבועות שלהם: השיטות באמצעות קול היו פגיעות להיות מושפע צלילים סביבתיים; השיטות באמצעות התנועה של כף היד היו שקשה להבדיל בין פעילויות גופניות אחרות, כאשר לא לצרוך מזון; השיטות שימוש תמונות ו- EMG אותות מוגבלים על ידי הגבול של התנועה ואת הסביבה. מחקרים אלו הראו את היכולת של זיהוי אוטומטי של צריכת המזון באמצעות חיישנים, אך עדיין יש מגבלה של ישימות מעשיים לחיי היומיום מעבר מעבדה הגדרות.
במחקר זה, השתמשנו דפוסי הפעילות שריר temporalis כמו אוטומטי ואובייקטיבי הפיקוח על צריכת המזון. באופן כללי, השריר temporalis חוזר להתכווצות והרפיה כחלק masticatory השריר במהלך ה מזון צריכת16,17; לפיכך, ניתן לנטר את פעילות צריכת מזון על ידי גילוי דפוסי הפעילות שריר temporalis תקופתי. לאחרונה, היו מספר מחקרים ניצול temporalis את השריר פעילות18,19,20,21, אשר השתמש EMG או זן פיזואלקטריים חיישן וצירופם ישירות על האדם העור. גישות אלה, עם זאת, היו רגישים למיקום העור של EMG אלקטרודות או חיישנים זן והיו בקלות מנותק מן העור עקב התנועה הפיזית או זיעה. לכן, אנחנו הציע שיטה חדשה ויעילה באמצעות זוג משקפיים חוש זה temporalis השריר בפעילות באמצעות טען שני תאים מוכנס בשני הצירים שלנו המחקר הקודם22. שיטה זו הראה פוטנציאל גדול של זיהוי הפעילות צריכת מזון עם רמת דיוק גבוהה בלי לגעת בעור. זה היה גם בלתי פולשנית ובלתי פולשנית, מאז היינו מכשיר משקפיים-סוג נפוץ.
במחקר זה, אנו מציגים סדרת הפרוטוקולים מפורט של איך ליישם את המשקפיים-סוג ההתקן וכיצד להשתמש דפוסי הפעילות שריר temporalis עבור ניטור צריכת המזון ופעילות גופנית. הפרוטוקולים לכלול התהליך של תכנון החומרה ועל פבריקציה נוספת המורכבת מודפס 3D מסגרת של המשקפיים, מודול מעגל מודול רכישת הנתונים וכלול את האלגוריתמים תוכנה עבור עיבוד נתונים וניתוח. יתר על כן נבחנו הסיווג בין מספר פעילויות נבחרים (למשל, לעיסה, הליכה, קריצות) כדי להדגים את הפוטנציאל כמערכת מעשי זה רואים הבדל דקה בין צריכת המזון ופעילות גופנית אחרת תבניות.

<table><tbody><tr><td>FSS1500NSB</td><td>Honeywell, USA</td><td></td><td>Load cell</td></tr><tr><td>INA125U</td><td>Texas Instruments, USA</td><td></td><td>Amplifier</td></tr><tr><td>ESP-07</td><td>Shenzhen Anxinke Technology, China</td><td></td><td>MCU with Wi-Fi module</td></tr><tr><td>74LVC1G3157</td><td>Nexperia, The Netherlands</td><td></td><td>Multiplexer</td></tr><tr><td>MP701435P</td><td>Maxpower, China</td><td></td><td>LiPo battery</td></tr><tr><td>U1V10F3</td><td>Pololu, USA</td><td></td><td>Voltage regulator</td></tr><tr><td>Ultimaker 2</td><td>Ultimaker, The Netherlands</td><td></td><td>3D printer</td></tr><tr><td>ColorFabb XT-CF20</td><td>ColorFabb, The Netherlands</td><td></td><td>Carbon fiber filament</td></tr><tr><td>iPhone 6s Plus</td><td>Apple, USA</td><td></td><td>Data acquisition device</td></tr><tr><td>Jelly Belly</td><td>Jelly Belly Candy Company, USA</td><td></td><td>Food texture for user study</td></tr></tbody></table>

design and evaluation of smart glasses for food intake and physical activity classification

מחקר זה מציג סדרה של פרוטוקולים של תכנון וייצור משקפיים-סוג התקן לביש מזהה דפוסי פעילות שרירים temporalis במהלך צריכת המזון ופעילויות גופניות אחרות. אנחנו מפוברק מודפס 3D מסגרת של המשקפיים ומודול עומס משולבת תא מעגל מודפס לוח (PCB) הוכנס בשני הצירים של המסגרת. המודול שימש כדי לרכוש את האותות כוח, ולהעביר אותם באופן אלחוטי. הליכים אלה מספקים את המערכת עם ניידות גבוהה יותר, אשר ניתן להעריך בתנאים לובש מעשיים כגון הליכה, מניד. הופעה של הסיווג מוערך גם על-ידי הבחנה דפוסי צריכת המזון של פעילות גופנית. סדרה של אלגוריתמי שימשו ליצירה של תהליך מוקדם את האותות, תכונה וקטורים, לזהות את הדפוסים של כמה נבחרים (לעיסה, קריצות), ופעילויות אחרות פעילות גופנית (לנוח בישיבה, מדבר, והליכה). התוצאות הראו כי הייתה התוצאה1 הממוצע F של הסיווג בין הפעילויות נבחרים 91.4%. אנו מאמינים שבגישה זו יכול להיות שעשוי להיות שימושי עבור אוטומטי ואובייקטיבי ניטור של התנהגויות ingestive עם דיוק גבוה יותר כאמצעי המעשי לטיפול בבעיות ingestive.

באמצעות ההליכים שפורטו בפרוטוקול, הכנו שתי גרסאות של המסגרת מודפס 3D על ידי הבחנה על אורך היצירה ראש, LH (133 ו- 138 מ"מ), ועל המקדשים, הT (110 ו- 125 מ מ), כפי שמוצג באיור4. לכן, שנכסה מספר תנאים לובש, אשר יכולים להיות מגוונים של הנבדקים גודל הראש, צורה, וכו</...

Watch this Scientific Journal Video about Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification at JoVE.com

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

תכנון והערכה של המשקפיים חכמים על צריכת המזון ועל פעילות גופנית סיווג

מחקר זה מציג פרוטוקול של תכנון וייצור משקפיים-סוג התקן לביש מזהה דפוסי צריכת המזון ומוספים אחרים פעילות גופנית נבחרים באמצעות טען תאים בשני הצירים של המשקפיים.

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle ...

design-evaluation-smart-glasses-for-food-intake-physical-activity

Universidad de Cartagena UDEC

Research

JoVE Journal

Engineering

11.2K Views.  Seoul National University. מחקר זה מציג פרוטוקול של תכנון וייצור משקפיים-סוג התקן לביש מזהה דפוסי צריכת המזון ומוספים אחרים פעילות גופנית נבחרים באמצעות טען תאים בשני הצירים של המשקפיים.

Video: Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

הערכה של מערכת פעילות אנושית המבוססת על טלפון חכם הכרה בימי סביבת חיים

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Behavior

Control of Eating Behavior Using a Novel Feedback System

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients.

Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

שליטה של אכילה, התנהגות באמצעות מערכת משוב הרומן

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve ...

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

פלטפורמה דיגיטלית חדשניים עבור תוכנית שיקום הלב ביתי תחת פיקוח

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

מתודולוגיה ללכידת תשומת לב ויזואלית משותפת באמצעות עוקבים אחר העין הניידת

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

פיתוח איטרטיבי של כלי הערכה תזונתי חדשני מבוסס סמארטפון: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

פיתוח קונספט ושימוש בשיטת הערכת צריכת מזון ואכילה אוטומטית

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to use. Therefore, new approaches like immersive analytics are welcome. Immersive analytics promise to experience high-dimensional data sets in a convenient manner for various user groups and data sets. Technically, virtual-reality devices are used to enable immersive analytics. In Industry 4.0, for example, scenarios like the identification of outliers or anomalies in high-dimensional data sets are pursued goals of immersive analytics. In this context, two important questions should be addressed for any developed technical solution on immersive analytics: First, is the technical solutions being helpful or not? Second, is the bodily experience of the technical solution positive or negative? The first question aims at the general feasibility of a technical solution, while the second one aims at the wearing comfort. Extant studies and protocols, which systematically address these questions are still rare. In this work, a study protocol is presented, which mainly investigates the usability for immersive analytics in Industry 4.0 scenarios. Specifically, the protocol is based on four pillars. First, it categorizes users based on previous experiences. Second, tasks are presented, which can be used to evaluate the feasibility of the technical solution. Third, measures are presented, which quantify the learning effect of a user. Fourth, a questionnaire&#160;evaluates the stress level when performing tasks. Based on these pillars, a technical setting was implemented that uses mixed reality smartglasses to apply the study protocol. The results of the conducted study show the applicability of the protocol on the one hand and the feasibility of immersive analytics in Industry 4.0 scenarios on the other. The presented protocol includes a discussion of discovered limitations.

This protocol delineates the technical setting of a developed mixed reality application that is used for immersive analytics. Based on this, measures are presented, which were used in a study to gain insights into usability aspects of the developed technical solution.

הערכת היבטי שימושיות של פתרון מציאות משולבת לניתוח מקיף בתרחישי תעשייה 4.0

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to ...

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

רשתות עצביות עמוקות להערכת תזונה מבוססת תמונה

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.

This paper discusses how to build a brain-computer interface by relying on consumer-grade equipment and steady-state visually evoked potentials. For this, a single-channel electroencephalograph exploiting dry electrodes was integrated with augmented reality glasses for stimuli presentation and output data visualization. The final system was non-invasive, wearable, and portable.

ממשק מוח-מחשב לביש חד-ערוצי ולא פולשני לתעשייה ולשירותי בריאות

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on ...

שיפור המעורבות בכיתה: ניתוח מבוסס בינה מלאכותית של רמות הקשב של התלמידים

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.

מחבר זרקור: התמודדות עם אתגרים טכניים וסובייקטיביים במדידת קשב בכיתה

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.

מערכת מבוססת בינה מלאכותית לאיתור רמות קשב אצל תלמידים

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