ISSN: 2032-927X
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2019
Issue 18
2018
Issue 17
2017
Issue 16Issue 15Issue 14Issue 13
2016
Issue 12Issue 11Issue 10Issue 9
2015
Issue 8Issue 7Issue 6Issue 5
2014
Issue 4Issue 3Issue 2Issue 1

This journal has been renamed to: EAI Endorsed Transactions on Bioengineering and Bioinformatics

Editor(s)-in-Chief: Gongliang Liu, Silvia Gabrielli and Tiziana Catarci

The design of Ambient Systems is a cross-disciplinary endeavour aimed at supporting and augmenting people’s activities in everyday life, by embedding computational and artificial intelligence in the distributed network of devices available in their living environment.

While technology needs to become more and more invisible to provide smart support, novel forms of user experience and multimodal interaction need to be devised to enable fluid, intuitive and appealing transitions between the physical and digital world.

This journal seeks contributions from leading experts and visionary thinkers in industry and research, about the principles, interaction paradigms, methods and applications that best can drive the future design of Ambient Systems and their human-centred, situated interfaces.

  • Methods and tools for the development of Pervasive Environmental infrastructure
  • Design of highly immersive contents for Ambient Media
  • Trustful mechanisms for context awareness, social sensing and inference
  • AIterative design and evaluation studies of multimodal interfaces
  • Adaptive and personalized solutions for ambient intelligence
  • Embodied agents and affective interfaces
  • Personal, social, safety and ethical issues raised by Ambient system applications, such as Domotics, Ambient Assisted Living, Pervasive Edutainment Environments
  • Andrea Marin (Università Ca' Foscari, Venezia) Ambient Pervasive Sensing and Applications
  • Aniruddha Bhattacharjya (Tsinghua National Laboratory for Information Science and Technology, Department of Electronics Engineering, Tsinghua University, Beijing, China)
  • Armir Bujari (Università degli Studi di Padova)
  • Artur Lugmayr (Curtin University)
  • Binh P. Nguyen (Senior Lecturer, School of Mathematics and Statistics, Victoria University of Wellington, New Zealan)
  • Huynh Xuan Hiep (Can Tho University, Vietnam)
  • Issam W. Damaj (College of Arts and Sciences, American University of Kuwait)
  • Chien-Chih Yu (National ChengChi University)
  • Vijender Kumar Solanki (CMR Institute of Technology, Autonomous, Hyderabad, TS, India)
  • Giacomo Cabri (Università di Modena e Reggio Emilia)
  • Gabriellie Peko (The University of Auckland)
  • Huynh Xuan Hiep (Can Tho University, Vietnam)
  • Juan Carlos Augusto (Middlesex University at London, UK)
  • Khoa X. Le (Ulster University, United Kingdom)
  • Loc Nguyen (Director of International Engineering and Technology Institute)
  • Krishna Asawa (Associate Professor)
  • Manik Sharma (DAV University, India)
  • Muhammad Athar Javed Sethi (University of Engineering and Technology Peshawar)
  • Nguyen Ha Huy Cuong (Quang Nam University)
  • Oscar Mayora (CREATE-NET, Trento, Italy)
  • Pedro Santos (Fraunhofer IGD, Germany)
  • Pietro Cipresso (Applied Technology for Neuro-Psychology Lab - IRCCS Istituto Auxologico Italiano)
  • Prashant Vats(FIMT, GGSIPU, Delhi, India)
  • Rohin Mittal (State University of New York at Buffalo, NY, United States)
  • Silvia Mirri (Università degli Studi di Bologna)
  • Shahed Mohammadi (Ragheb Isfahani Higher Education Institute, Iran)

  • Hypoglycemic Detection by Human Breath: A Mobile Health App that Alerts Diabetics of Low Blood Glucose

    Appears in:
    amsys 19(18): e4

    Authors:
    M. Agarwal, H. Vatani, A. Faiola

    Published:
    12nd Dec 2019

    Abstract:
    Low blood glucose (BG) or hypoglycemia (HYPO) can lead to severe health complications such as weakness and unconsciousness. To avoid problems BG self-management is needed. We developed a non-invasive breathing system (HYPOalert) to detect HYPO in human-breath, that sends warning alerts and data visualization to monitor progress. This paper presents two HYPOalert prototype iterations with testing results. Of 14 Type 1/2 diabetics tested, only 10% were pleased with existing monitoring systems and 85% expressed interest in using HYPOalert more than 20x a day. The usability study showed that 92% agreed-strongly agreed with the HYPOalert design, including color/menus/navigation/typography; and 64% felt positive about the apps consistency, flexibility, and info architecture. A post-test survey provided a satisfaction score: 6.64/10, with an open-ended interview showing that HYPOalert could positively impact lifestyle practices, self-managing, and help advance an understanding of the disease.

  • Sensor Modalities and Fusion for Robust Indoor Localisation

    Appears in:
    amsys 19(18): e5

    Authors:
    Raúl Santos-Rodríguez, Michał Kozłowski, Robert J. Piechocki

    Published:
    12nd Dec 2019

    Abstract:
    The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.

  • Activity recognition evaluation via machine learning

    Appears in:
    amsys 19(18): e3

    Authors:
    A.M. Connor, J. Kruse, A.N.A. Rameka

    Published:
    15th Nov 2019

    Abstract:
    With the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can work in unison to react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Activity recognition is already a widely researched area and could provide a more direct way of including humans within this system. This research explores the feasibility of using a cost effective, unobtrusive, single modality ground-based sensor matrix to track subtle pressure changes to predict user activity, in an effort to assess its ability to act as an intermediary interface between humans and digital systems such as the IoT.

  • A search engine dedicated to the environment around the Congo basin area: Model and Architecture

    Appears in:
    amsys 19(18): e2

    Authors:
    Ousmane Sall, Belmond Djomo, Marie N’diaye, Mouhamadou Saliou Diallo

    Published:
    14th Jan 2019

    Abstract:
    Considering the environmental field around the Congo Basin area (in Central Africa), many initiatives implement and communicate on their activities via the internet, yet the information they provide remains very little exploited, even inaccessible. The data is scattered on the internet and it is quite difficult to find information in a fairly precise way. Despite the existence of Internet research services (search engines) developed to facilitate the search for information in the vast data network that is the Internet, there are still concerns about quality, and the relevance of information provided in as research results. In this context, we present in this paper a construction approach and the architecture of a search engine dedicated to the environment around the Congo Basin area. This paper develops a theoretical approach that uses scientific analysis and empirical approaches to conceptualize the optimization of the relevance of the results of a thematic search engine by aggregating tools. This approach is a compilation of ideas, methods and tools that, put together, will improve the relevance of the results of a thematic research.

  • Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network

    Appears in:
    amsys 18(18): e1

    Authors:
    Steven Schmitt, Farah I. Kandah

    Published:
    23rd Aug 2018

    Abstract:
    Recent trends have shown a migration of software from local machines to server-based services. These service-based networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity.

  • An Integrated Haptic System combining VR, a Markerless Motion Capture System & Tactile Actuators

    Appears in:
    amsys 18(17): e4

    Authors:
    A.T. Maereg, D. Reid, A.K. Nagar, E.L. Secco

    Published:
    23rd Mar 2018

    Abstract:
    In the industrial environments, it is common that robotic or remote interaction with both rigid objects and soft or deformable objects is required. However, it is usual in such an environment that only one mode of manipulation is used, and that little or no distinction is made between rigid or deformable objects. The ability to “feel” or touch an object easy a naturalistic way to determine what type of object is being manipulated. By feeling an object appropriate manipulation techniques can be applied. A novel Virtual Reality (VR) interface is presented that incorporates tactile feedback in order to “feel” objects being manipulated. Incorporation of an important extra “sense” into such a system allows far more nuanced and dexterous interaction to occur in manufacturing environments that may be “messy”, have imprecisely located objects or that have a range of different materials present.

  • Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying

    Appears in:
    amsys 18(17): e2

    Authors:
    Christopher Azzara, Nikhil Yadav, Fazel Keshtkar, Mehrdad Aliasgari

    Published:
    23rd Mar 2018

    Abstract:
    The ubiquitous nature of mobile devices like smartphones and tablets make them ideal platforms for engaging users in Ecological Momentary Assessments (EMA). In EMA, participants are repeatedly assessed frequently (daily or multiple times per day) through a set of questionnaires. Fluctuations in psychological states, such as cognition and e ect can be recorded in real time using mobile devices. EMA results can further be coupled with other physiological sensor data procured through wearables and smartphones, to validate and correlate patient experiences and responses to certain treatments and medications. This can be useful for health care organizations which are interested in the impact of their treatment techniques on patient populations. In this paper, we present an EMA platform developed using Android mobile devices. The collected results are shown and techniques used to query the data are demonstrated. The platform is flexible and can scale up to perform data mining algorithms for sentiment analysis based on the stimulus to a medication or treatment over a prolonged period of time.

  • Using the Internet of Things to Support Emotional Health

    Appears in:
    amsys 18(17): e1

    Authors:
    Nancy L. Russo, Javier Marin, Jeanette Eriksson

    Published:
    23rd Mar 2018

    Abstract:
    A first step towards emotional well-being is to monitor, understand and reflect upon one’s feelings and emotions. A number of personal emotion-tracking applications are available today. In this paper we describe an examination of these applications which indicates that many of the applications do not provide sufficient support for monitoring a full spectrum of emotional data or for analysing or using the data that is provided. To design applications that better support emotional well-being, the full capabilities of the Internet of Things should be utilized. The paper concludes with a description of how Internet of Things technologies can enable the development of systems that can more accurately capture emotional data and support personal learning in the area of emotional health.

  • Food Consumption Tracker with Health Advises by Food Photos and Labels

    Appears in:
    amsys 18(17): e3

    Authors:
    Geoffrey Poon, Vane-Ing Tian, Wai-Man Pang

    Published:
    23rd Mar 2018

    Abstract:
    Obesity, overweight, allergy and additives related disease are common health problems worldwide nowadays. Many of these problems directly relate to food consumption and dietary habits. Hence keep tracking food intake and knowing the consumed food ingredients are essential for a healthy life. However, tracking daily consumption is tedious, and seldom be kept for long. This paper presented a mobile solution to leverage these troublesome and provide health advices on food consumption adaptively. Users can simply take photos of their meals as they usually do for dietary record. Our system then automatically recognizes the food type and provides useful statistics for analysis. We can know better the packaged food with barcode scanning and text recognition of food labels. All captured information provide basis for proper alert on allergen, suggestions to achieve health and balanced dietary. A preliminary study reveals that this is welcomed by most users care about their dietary.

  • Heart Disease Diagnosis Based on Deep Radial Basis Function Kernel Machine

    Appears in:
    amsys 18(17): e5

    Authors:
    Saad Amin, Vasile Palade, Mashail Alsalamah

    Published:
    23rd Mar 2018

    Abstract:
    Over the years Radial Basis Function (RBF) Kernel Machines have been used in Machine Learning tasks, but there are certain flaws that prevent their usage in some up-to-date applications (e.g., some Kernel Machines suffer from fast growth number of learning parameters whilst predicting data with large number of variations). Besides, Kernel Machines with single hidden layer have no mechanisms for features selection in multidimensional data space, and machine-learning task becomes intractable with enlargement of the data available for analysis. To address these issues, this paper investigates the usage of a framework for “deep learning” architecture composed of multilayered adaptive non-linear components – Multilayer RBF Kernel Machine. To be precise, three different approaches of features selection and dimensionality reduction to train RBF based on Multilayer Kernel Learning are explored, and comparisons between them in terms of accuracy, performance and computational complexity are made. As opposed to the “shallow learning” algorithm with usually single layer architecture, results show that the multilayered system produces better results with large and highly varied data. In particular, features selection and dimensionality reduction, as a class of the multilayer method, shows results that are more accurate. This paper proposes a novel scheme based on deep Multilayer RBF Kernel Machine learning for sleep apnea detection and quantification using statistical features of ECG signals. The results obtained show that the newly proposed approach provides significant accuracy improvements compared to state-of-the-art methods. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.

  • Accessible and Usable Websites and Mobile Applications for People with Autism Spectrum Disorders: a Comparative Study

    Appears in:
    amsys 17(13): e5

    Authors:
    Flaminia L. Luccio, Antonina Dattolo

    Downloads:
    2794

    Abstract:
    Accessibility, usability and inclusion represent desirable challenges of current research in the field of universal design: in some cases, these features require adaptive behaviours and specialised customisations, while, in general, it is possible to identify common and hareable guidelines. We focus our attention on hildren with autism spectrum disorders. Many studies show the positive impact of using computer technologies for supporting the lives of these users. Despite that, just a restricted part of the current websites and apps is accessible and usable for people with ASD. In this paper, we present general and shared guidelines and best practices for accessibility and usability for all; and we propose specialised guidelines for designers and developers of websites and mobile applications for users with ASD. We then present a review of many of the existing websites and applications, in order to check which comply with all, or parts of these guidelines.

  • Clothing Style Recognition using Fashion Attribute Detection

    Appears in:
    amsys 15(5): e3

    Authors:
    Xiao Wu, Qiang Peng, Guang-Lu Sun, Hong-Han Chen

    Downloads:
    2587

    Abstract:
    In this paper, a new framework is proposed for clothing style recognition in natural scenes. Clothing region is first detected through the fusion of super-pixel segmentation, saliency detection and Gaussian Mixture Model (GMM). Next, a group of fashion attribute detectors are trained to get the likelihood of each attribute in the clothing image. Finally, the correlation matrix between clothing styles and fashion attributes is adopted to predict the clothing style. For evaluation, we collect a dataset for clothing style recognition which contains 5 styles and 14 fashion attributes. Extensive experiments demonstrate that the proposed framework has a promising ability to recognize the clothing style.

  • Muscle Strength Testing using Wearable Wireless Sensors

    Appears in:
    amsys 15(10): e2

    Authors:
    DK Arvind, Debadri Mukherjee

    Downloads:
    2556

    Abstract:
    Manual muscle testing and its variants have a long history of use for classifying muscle strengths. For the first time, inexpensive wearable wireless sensors combined with machine learning techniques are used to classify different levels of muscle strength, which addresses some limitations of the manual method. A mean accuracy of 93% was obtained across ten subjects using gyroscope and accelerometer data in classifying four distinct levels of strengths of the biceps brachii muscle when performing muscle contraction under appropriate load. This was reduced by 2% for accelerometer-only data, thus offering a potentially inexpensive and viable solution for testing muscle strength.

  • Designing Intuitive Web Solutions for Monitoring Patients’ Rehabilitation at Home

    Appears in:
    amsys 15(8): e3

    Authors:
    Rosa Maimone, Raihana Ferdous, Silvia Gabrielli

    Downloads:
    2346

    Abstract:
    This paper describes the design of web-based remote monitoring interfaces aimed at supporting therapists and caregivers in supervising motor-cognitive rehabilitation plans of care to be performed by patients at home. This work was part of a three years’ research project where game-based environments for upper body motor rehabilitation of post-stroke patients were developed in collaboration with two main rehabilitation centers in Italy and Austria, for a subsequent deployment at patients’ homes. The paper will specifically focus on describing the iterative design of the home rehabilitation features for clinicians over the first two years of the project to enable the delivery and monitoring of more personalized, engaging plans of care for home therapy by rehabilitation centers and services.

  • FM-CW radar sensors for vital signs and motor activity monitoring

    Appears in:
    amsys 11(1): e5

    Authors:
    Gabriela Postolache, Octavian Adrian Postolache, José Miguel Costa Dias Pereira, Pedro Manuel Brito da Silva Girão

    Downloads:
    2188

    Abstract:
    The article summarizes on-going research on vital signs and motor activity monitoring based on radar sensors embedded in wheelchairs, walkers and crutches for in home rehabilitation. Embedded sensors, conditioning circuits, real-time platforms that perform data acquisition, auto-identification, primary data processing and data communication contribute to convert daily used objects in home rehabilitation into smart objects that can be accessed by caregivers during the training sessions through human–machine interfaces expressed by the new generation of smart phones or tablet computers running Android OS or iOS operating systems. The system enables the management of patients in home rehabilitation by providing more accurate and up-to-date information using pervasive computing of vital signs and motor activity records.

  • IoT and Connected Insurance Reshaping The Health Insurance Industry. A Customer-centric “From Cure To Care” Approach

    Appears in:
    amsys 17(15): e5

    Authors:
    A. Silvello

    Downloads:
    2019

    Abstract:
    An increasing global population, the rise in number of chronic disease patients and the threat of global epidemics have made the way for technology as a potential answer to many of these problems. Health insurance can contribute to the resolution of some of these issues but insurers need to transition from simple “Payers” to “Players” in order to achieve that. They need to become points of reference on which the customer and the health care system can count on. This is possible and is strictly related to connected insurance and in particular to wearables and devices that are able to gather vital data from patients and share them with the care givers.

  • Harmony: A Hand Wash Monitoring and Reminder System using Smart Watches

    Appears in:
    amsys 15(6): e4

    Authors:
    John A. Stankovic, Md Abu Sayeed Mondol

    Downloads:
    1952

    Abstract:
    Hand hygiene compliance is extremely important in hospitals, clinics and food businesses. Caregivers' compliance with hand hygiene is one of the most effective tools in preventing healthcare associated infections (HAIs) in hospitals and clinics. In food businesses, hand hygiene compliance is essential to prevent food contamination, and thus food borne illness. Washing hands properly is the cornerstone of hand hygiene. However, the hand wash compliance rate by the workers (care givers, waiters, chefs, food processors and so on) is not up to the mark. Monitoring hand wash compliance along with a reminder system increases the compliance rate significantly. Quality of a hand wash is also important which can be achieved by washing hands in accordance with standard guidelines. In this paper, we present Harmony, a hand wash monitoring and reminder system that monitors hand wash events and their quality, provides real time feedback, reminds the person of interest when he/she is required to wash hands, and stores related data in a server for further use. Worker worn smart watches are the key components of Harmony that can differentiate hand wash gestures from other gestures with an average accuracy of about 88%. Harmony is robust, scalable, and easy to install, and it overcomes most of the problems of existing related systems.

  • Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network

    Appears in:
    amsys 18(18): e1

    Authors:
    Steven Schmitt, Farah I. Kandah

    Downloads:
    1746

    Abstract:
    Recent trends have shown a migration of software from local machines to server-based services. These service-based networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity.

  • Android-Stego: A Novel Service Provider Imperceptible MMS Steganography Technique Robust to Message Loss

    Appears in:
    amsys 15(5): e2

    Authors:
    Justin Shi, Jie Wu, Avinash Srinivasan

    Downloads:
    1704

    Abstract:
    Information hiding techniques, especially steganography, have been extensively researched for over two decades. Nonetheless, steganography on smartphones over cellular carrier networks is yet to be fully explored. Today, smartphones, which are at the epitome of ubiquitous and pervasive computing, make steganography an easily accessible covert communication channel. In this paper, we propose Android-Stego - a framework for steganography employing smart-phones. Android-Stego has been evaluated and confirmed to achieve covert communication over real world cellular service providers' communication networks such as Verizon and Sprint. A key contribution of our research presented in this paper is the benchmark results we have provided by analyzing real world cellular carriers network restrictions on MMS message size. We have also analyzed the actions the carriers take - such as compression and/or format conversion - on MMS messages that fall outside the established MMS communication norm, which varies for each service provider. Finally, We have used these benchmark results in implementing Android-Stego such that it is sensitive to carrier restrictions and robust to message loss.

  • Gait Parameters Change Prior to Freezing in Parkinson's Disease: A Data-Driven Study with Wearable Inertial Units

    Appears in:
    amsys 15(10): e4

    Authors:
    Sinziana Mazilu, Gerhard Tröster, Maria Laura Ferster

    Downloads:
    1652

    Abstract:
    Freezing of gait (FoG) is a motor impairment among patients with advanced Parkinson's disease which is associated with falls and has a negative impact on a patient's quality of life. Wearable systems have been developed to detect FoG and to help patients resume walking by means of rhythmical cueing. A step further is to predict the FoG and start cueing a few seconds before it happens, which might help patients avoid the gait freeze entirely. We characterize the gait parameters continuously with up to 10-12 seconds prior to FoG, observe if and how they change before subjects enter FoG, and compare them with the gait before turns. Moreover, we introduce and discuss new frequency-based features to describe gait and motor anomalies prior to FoG. Using inertial units mounted on the ankles of 5 subjects, we show specific changes in the stride duration and length with up to four seconds prior to FoG on all subjects, compared with turns. Moreover, the dominant frequency migrates towards [3, 8] Hz band with up to six seconds prior to FoG on 3 subjects. These findings open the path to real-time prediction of FoG from inertial measurement units.
Publisher
EAI
ISSN
2032-927X
Number of Volumes
6
Last Published
2019-12-12
EAI Logo

About EAI

Community

Publish with EAI