この作品は、Envisible、株式会社によって支えられました。本研究は韓国健康技術 R & D プロジェクト、厚生・福祉、韓国共和国 (HI15C1027) の助成金によっても支えられました。この研究は、韓国の国立研究財団 (NRF 2016R1A1A1A05005348) によっても支えられました。

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著者が明らかに何もありません。

この研究では、まず設計と食品の摂取量と身体活動のパターンを感じるガラスの製造プロセスを提案しました。この研究として (歩行、まばたきなど) その他の身体活動から食物摂取を区別するためにデータ分析を中心に、センサーとデータ集録システムの移動記録の実装が必要です。したがって、システムには、センサー、無線通信機能を備えた MCU とバッテリーが含まれています。提案プ?...

継続的かつ客観的は食物摂取の監視は、過剰なエネルギーの蓄積は、overweightness と肥満1、様々 な医療の合併症2になる可能性を引き起こす可能性があります人間の体のエネルギー バランスを維持するために不可欠です。エネルギーの不均衡の主な要因は過剰な食物摂取および十分な身体活動3知られています。毎日のエネルギーの支出の監視の様々 な研究は、でもウェアラブル デバイス4,5,6, 身体活動パターンの自動と客観的測定を導入されている、最終消費者レベルと医療のステージ7。食物摂取量のモニタリングに関する研究はしかし、まだ、研究室の設定に、直接かつ客観的に食品の摂取量の活動を検出することは困難だからです。ここでは、デバイスの設計とその評価食品摂取量と日常生活の中で実践的なレベルで身体活動パターンを監視するための提示を目指します。
咀嚼・嚥下音8,9,10、手首11,12,13の動きを通じて食品の摂取量を監視し、画像に様々 な間接アプローチがあった分析14、および筋電図 (EMG)15。ただし、これらのアプローチが彼らの固有の制限があるため毎日の生活アプリケーションに適用することは困難: 音を使用してメソッドが環境音によって影響を受ける脆弱性食物を消費していない場合他の身体活動から区別するために、手首の動きを使用してメソッドが難しかった画像と筋電位信号を用いた方法は、運動と環境の境界によって制限されます。これらの研究は、センサーを使用して食品の摂取量の自動検出の機能を示したが、まだ実験室の設定を超えて日常生活に実用性の制限があった。
この研究では、食品摂取量の自動かつ客観的監視に側頭筋筋活動のパターンを使用しました。一般的に、側頭筋を繰り返し収縮と食品摂取量16,17; 中に咀嚼筋の一部としてリラクゼーションしたがって、側頭筋の筋活動の周期的なパターンを検出することにより食品摂取量の活動を監視できます。最近、活動18,19,20,21, 筋電図や圧電歪に使用筋肉の側頭筋を利用したいくつかの研究をされている人間の上に直接適用してセンサー皮膚。これらのアプローチは、しかし、筋電図電極またはひずみセンサーの皮膚の場所に敏感だった、運動や汗のため皮膚から簡単にデタッチされました。したがって、側頭筋筋活動両方のヒンジを所定の私たちの以前の研究22で挿入する 2 つのロード セルその意味提案メガネのペアを使用して新しい、有効な方法です。この方法は、肌に触れることがなく高精度な食品摂取量の活動を検出の大きな可能性を示した。また非目障りな非侵入以来だった我々 は一般的なメガネ型デバイスを使用します。
本研究では、メガネ型デバイスを実装する方法と食品の摂取量と身体活動を監視するため側頭筋筋活動のパターンを使用する方法の詳細なプロトコルのシリーズを紹介します。プロトコルは、ハードウェア設計とメガネ、回路モジュール、およびデータ集録モジュールの 3 D プリントのフレームで構成される製造のプロセスを含めるし、データ処理・解析用ソフトウェア アルゴリズムを含めます。さらにいくつかの注目の活動 (例えば、咀嚼、歩行とウインク) の中で分類を行った食品摂取量と他の身体活動の分の違いを伝えることができる実用的なシステムとしての可能性を実証するにはパターン。

<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

本研究は、一連の設計および製造食品の摂取量と他の身体活動の間に側頭筋筋活動のパターンを検出するメガネ型ウェアラブル デバイスのプロトコルを示します。メガネとフレームの両方のヒンジに挿入された負荷セル統合プリント回路基板 (PCB) モジュールの 3 D プリントのフレームを作製しました。力信号を集録し、ワイヤレスでそれらを送信モジュールが使用されました。これらの手順は、歩行や waggling といった実用的な身に着けている条件で評価できる高いモビリティとシステムを提供します。分類のパフォーマンスは、食物摂取のパターンを区別するこれらの物理的な活動によっても評価されます。一連のアルゴリズムを用いて信号を前処理特徴ベクトルを生成し、いくつかのパターンを認識する活動 (咀嚼とウインク)、および他の物理的な活動 (、話して、歩いて、残りを定住) を特集します。注目の活動の中で分類の平均 F1スコア 91.4% であったことがわかった。このアプローチは潜在的自動かつ客観的摂食問題を扱うための実用的な手段として精度の高い摂食行動の監視のために有用することができます考えています。

、プロトコルで説明されている手順を、図 4に示すように頭の部分、LH (133 と 138 mm) や寺院、LT (110 と 125 mm) の長さの差別化で 3 D 印刷されたフレームの 2 つのバージョンを準備しました。そのため、被験者の頭のサイズ、形状など対象を選んだユーザー研究のため自分の頭に合わせてフレームの一つから変えることができ?...

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

デザインとスマートなメガネの食品の摂取量と身体活動の分類のための評価

本稿ではプロトコルの設計と食物摂取のパターンを検出するメガネ型ウェアラブル デバイスを製造、ガラスの両方のヒンジのロードセルを使用してその他の注目の物理的な活動が挿入されます。

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

Research

JoVE Journal

Engineering

11.1K ビュー.  Seoul National University. 本稿ではプロトコルの設計と食物摂取のパターンを検出するメガネ型ウェアラブル デバイスを製造、ガラスの両方のヒンジのロードセルを使用してその他の注目の物理的な活動が挿入されます。

ビデオ: 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.

日常生活環境におけるスマートフォンベースの人間活動の認識システムの評価

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

行動学

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.

新しいフィードバック システムを使用して食生活のコントロール

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.

監視対象の在宅心臓リハビリテーション プログラムの新規デジタル プラットフォーム

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.

モバイルアイトラッカーを用いた関節視覚注意を捉える方法論

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.

革新的なスマートフォンベースの食事評価ツールの反復開発: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.

自動食品摂取と摂食行動評価方法のコンセプト開発と利用

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.

インダストリー 4.0 シナリオにおける没入型分析のための複合現実ソリューションのユーザビリティの側面の評価

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

工学

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.

画像ベースの食事評価のためのディープニューラルネットワーク

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.

産業およびヘルスケア向けの単一チャネルおよび非侵襲的ウェアラブルブレインコンピュータインタフェース

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 ...

教室でのエンゲージメントの向上:AIによる生徒の注意力レベルの分析

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.

著者スポットライト:教室の注目度を測定する際の技術的および主観的な課題への対処

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.

人工知能を用いた学生の注意力検出システム

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

デザインとスマートなメガネの食品の摂取量と身体活動の分類のための評価

要約

さらに動画を探す

この動画の章

日常生活環境におけるスマートフォンベースの人間活動の認識システムの評価

新しいフィードバックシステムを使用して食生活のコントロール

監視対象の在宅心臓リハビリテーションプログラムの新規デジタルプラットフォーム

モバイルアイトラッカーを用いた関節視覚注意を捉える方法論

革新的なスマートフォンベースの食事評価ツールの反復開発:Traqq

自動食品摂取と摂食行動評価方法のコンセプト開発と利用

インダストリー 4.0 シナリオにおける没入型分析のための複合現実ソリューションのユーザビリティの側面の評価

画像ベースの食事評価のためのディープニューラルネットワーク

産業およびヘルスケア向けの単一チャネルおよび非侵襲的ウェアラブルブレインコンピュータインタフェース

人工知能を用いた学生の注意力検出システム

デザインとスマートなメガネの食品の摂取量と身体活動の分類のための評価

要約

さらに動画を探す

この動画の章

日常生活環境におけるスマートフォンベースの人間活動の認識システムの評価

新しいフィードバック システムを使用して食生活のコントロール

監視対象の在宅心臓リハビリテーション プログラムの新規デジタル プラットフォーム

モバイルアイトラッカーを用いた関節視覚注意を捉える方法論

革新的なスマートフォンベースの食事評価ツールの反復開発:Traqq

自動食品摂取と摂食行動評価方法のコンセプト開発と利用

インダストリー 4.0 シナリオにおける没入型分析のための複合現実ソリューションのユーザビリティの側面の評価

画像ベースの食事評価のためのディープニューラルネットワーク

産業およびヘルスケア向けの単一チャネルおよび非侵襲的ウェアラブルブレインコンピュータインタフェース

人工知能を用いた学生の注意力検出システム

新しいフィードバックシステムを使用して食生活のコントロール

監視対象の在宅心臓リハビリテーションプログラムの新規デジタルプラットフォーム