Hussein Al Osman
Bio: Hussein Al Osman is an academic researcher from University of Ottawa. The author has contributed to research in topics: Cloud gaming & Quality of experience. The author has an hindex of 15, co-authored 56 publications receiving 799 citations.
TL;DR: To the best of the knowledge, this is the first approach on mobile authentication that uses ECG biometric signals and it shows a promising future for this technology, although further improvements are still needed to optimize accuracy while maintaining a short acquisition time for authentication.
Abstract: Traditional mobile login methods, like numerical or graphical passwords, are vulnerable to passive attacks. It is common for intruders to gain access to personal information of their victims by watching them enter their passwords into their mobile screens from a close proximity. With this in mind, a mobile biometric authentication algorithm based on electrocardiogram (ECG) is proposed. With this algorithm, the user will only need to touch two ECG electrodes (lead I) of the mobile device to gain access. The algorithm was tested with a cell phone case heart monitor in a controlled laboratory experiment at different times and conditions with ten subjects and also with 73 records obtained from the Physionet database. The obtained results reveal that our algorithm has 1.41% false acceptance rate and 81.82% true acceptance rate with 4 s of signal acquisition. To the best of our knowledge, this is the first approach on mobile authentication that uses ECG biometric signals and it shows a promising future for this technology. Nonetheless, further improvements are still needed to optimize accuracy while maintaining a short acquisition time for authentication.
TL;DR: A comparison between two approaches to HRV measurement, one which is based on the independent component analysis (ICA) and the other based on Eulerian video magnification (EVM), showing that the proposed ICA-based method yields better results when it comes to the high frequency (HF) and low frequency over high-frequency (LF/HF) HRV parameters.
Abstract: Medical researchers have always been interested in heart rate (HR) and heart rate variability (HRV) analysis. However, nowadays, investigators from a variety of other fields are also probing the subject. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through HRV. Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human’s face through a camera. The latest approaches apply independent component analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian video magnification (EVM) to detect subtle changes in skin color associated with the PPG. To the best of our knowledge, EVM has not been successfully employed to extract HRV features from a video of a human face. In this paper, we present a comparison between our two approaches, one which is based on the ICA and the other is based on EVM. Final results show that the proposed ICA-based method yields better results when it comes to the high frequency (HF) and low frequency over high-frequency (LF/HF) HRV parameters [mean absolute error (MAE) of 0.57 and 0.419] when compared with the EVM-based method (MAE 0.76 and 1.69); however, the second method showed better MAE results for low frequency (LF) and higher correlation with the ground truth. Also our proposed ICA method showed better results in general by improving HF estimates, but the EVM-based method might be more appropriate when motion is involved or when the HF component is not important.
TL;DR: An overview of the recent achievements in affective haptics is presented and a thorough discussion about the effectiveness of using the haptic channel to communicate affective information through direct and mediated means is provided.
Abstract: Touch plays a prominent role in communicating emotions and intensifying interpersonal communication. Affective haptics is an emerging field, which focuses on the analysis, design, and evaluation of systems that can capture, process, or display emotions through the sense of touch. The objective of this paper is to present an overview of the recent achievements in affective haptics and to discuss how the sense of touch can elicit or influence human emotions. We first introduce a definition to the term affective haptics and describe its multidisciplinary nature—as a field that integrates ideas from affective computing, haptic technology, and user experience. Second, we provide a thorough discussion about the effectiveness of using the haptic channel to communicate affective information through direct and mediated means. Third, we present a variety of applications in the area ranging from interhuman social interaction systems to human robot interaction applications. Finally, we discuss some of the key findings discerned from the various surveyed papers, and present some of the challenges and trends in this field. We extract the following conclusions pertaining to affective haptics: 1) haptic stimulation can be successfully used to achieve a higher level of emotional immersion during media consumption or emotional telepresence; 2) existing research has demonstrated that haptics is effective in communicating valence and arousal, and the emotions of happiness, sadness, anger and fear, and less focus have been given to the communication of disgust and surprise; 3) the haptic-based affect detection remains an understudied topic, whereas the haptic-based affect display is a well-established subject; and 4) the interpretation of the haptic stimulation by human beings is highly contextual.
TL;DR: The potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm and proposing a recommendation algorithm to improve the user experience and satisfaction with the use of a biosignal in the recommendation process is highlighted.
Abstract: With the rapid increase of social media resources and services, Internet users are overwhelmed by the vast quantity of social media available. Most recommender systems personalize multimedia content to the users by analyzing two main dimensions of input: content (item), and user (consumer). In this study, we address the issue of how to improve the recommendation and the quality of the user experience by analyzing the contextual aspect of the users, at the time when they wish to consume multimedia content. Mainly, we highlight the potential of including a user's biological signal and leveraging it within an adapted collaborative filtering algorithm. First, the proposed model utilizes existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. Second, we propose a recommendation algorithm to improve the user experience and satisfaction with the use of a biosignal in the recommendation process. Our experimental results show the feasibility of personalizing the recommendation according to the user's context, and demonstrate some improvement on cold start situations where relatively little information is known about a user or an item.
TL;DR: The ability of the game feedback to assist players in modulating their behavior to reduce their stress levels is assessed and it is shown that the majority of participating subjects showed more control over their mental stress when game feedback was enabled.
Abstract: Serious games augment utilitarian applications with an entertainment dimension. Hence, information pertaining to a utilitarian objective is seamlessly incorporated into the gaming scenario. In this paper, we present the concept of ubiquitous biofeedback serious games (UBSGs), a family of games that integrate biofeedback processes in their operation. They rely on physiological inputs collected from the player through biological sensors for game control. These physiological inputs are converted into quantifiable parameters that reflect the status of a certain physiological process. To prove the practicality of this concept, we develop a UBSG aimed at providing mental stress management services to players. We assess the ability of the game feedback to assist players in modulating their behavior to reduce their stress levels. In our evaluation, we have shown that the majority of participating subjects showed more control over their mental stress when game feedback was enabled.
TL;DR: McNeill as discussed by the authors discusses what Gestures reveal about Thought in Hand and Mind: What Gestures Reveal about Thought. Chicago and London: University of Chicago Press, 1992. 416 pp.
Abstract: Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.
01 Jan 2012
01 Jan 2016
TL;DR: It’s time to dust off the gloves and get ready for the cold weather.
Abstract: １ インフラを構築する（ＡＷＳにおけるインフラ；ＶＰＣを構成する；ＶＰＣとオンプレミス環境とを接続する） ２ ファイルオブジェクトを保存・共有・公開する（オブジェクトストレージＳ３の機能；ファイルストレージとして利用する；Ｗｅｂサーバーを構築する；信頼性とコストのバランスをとりたい） ３ アプリケーションサーバーを構築する（Ａｍａｚｏｎ ＥＣ２とＡＷＳ Ｌａｍｂｄａ；スケーラビリティーを高める；サーバーレスでプログラムを動かす；データベースサービスを活用する） ４ ＡＷＳシステムを管理する（リソース監視と異常検知・通報；耐障害性を高める仕組みとバックアップ＆リカバリー；構成管理）
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
Abstract: The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the heart. In the literature, the ECG signal has been analyzed and utilized for various purposes, such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion recognition and biometric identification. ECG analysis (depending on the type of the analysis) can contain several steps, such as preprocessing, feature extraction, feature selection, feature transformation and classification. Performing each step is crucial for the sake of the related analysis. In addition, the employed success measures and appropriate constitution of the ECG signal database play important roles in the analysis as well. In this work, the literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above. Each step in ECG analysis is briefly described, and the related studies are provided.
TL;DR: It is argued that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient.
Abstract: Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.