Research and Development of a Cyber-I Open Service Platform

研究成果の概要 (和文) : Cyber-Iは、Real-Iのデジタル対応であり、個人データの収集と分析を行い、人の行動や感情に近づけます。本研究では、複数のデバイスから多くの個人データを収集して処理を行い、Cyber-Iの作成と管理を行うようなオープンサービスプラットフォームを開発しました。異なるデバイスやデータを柔軟でスケーラブルな管理をするために、スマートフォンをゲートウェイとして使用するクラウドやフォグベースのデータベースシステムを実装しています。また、Cyber-Iの成長をコントロールするような基本的技術やメカニズムを提案しています。さらにCyber-I関連における個人情報の保護と利用についても検討しています。研究成果の概要 (英文) : Cyber-I, short for Cyber Individual, is a digital counterpart of Real-Individual (Real-I), and is expected to continuously approximate a real person’s behavior and even mind with collections and analyses of increasing personal data. In this research, a Cyber-I open service platform has been researched and developed to collect and process rich personal big data from various sources and multiple devices for Cyber-I creation and administration as well as its modeling and life control. A cloud-fog based database system using smartphones as gateways has been implemented for flexible and scalable managements of heterogeneous devices and data. Basic strategy and mechanism have been proposed for scheduling and controlling Cyber-I growth. Cyber-I related data privacy protection and personal information usage are also studied. A series of researches on personality and affective computing has been carried out to model personal characteristics

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice

Programming frameworks for mobile sensing

The proliferation of smart mobile devices in people’s daily lives is making context-aware computing a reality. A plethora of sensors available in these devices can be utilized to understand users’ context better. Apps can provide more relevant data or services to the user based on improved understanding of user’s context. With the advent of cloud-assisted mobile platforms, apps can also perform collaborative computation over the sensing data collected from a group of users. However, there are still two main issues: (1) A lack of simple and effective personal sensing frameworks: existing frameworks do not provide support for real-time fusing of data from motion and visual sensors in a simple manner, and no existing framework collectively utilizes sensors from multiple personal devices and personal IoT sensors, and (2) a lack of collaborative/distributed computing frameworks for mobile users. This dissertation presents solutions for these two issues. The first issue is addressed by TagPix and Sentio, two frameworks for mobile sensing. The second issue is addressed by Moitree, a middleware for mobile distributed computing, and CASINO, a collaborative sensor-driven offloading system. TagPix is a real-time, privacy preserving photo tagging framework, which works locally on the phones and consumes little resources (e.g., battery). It generates relevant tags for landscape photos by utilizing sensors of a mobile device and it does not require any previous training or indexing. When a user aims the mobile camera to a particular landmark, the framework uses accelerometer and geomagnetic field sensor to identify in which direction the user is aiming the camera at. It then uses a landmark database and employs a smart distance estimation algorithm to identify which landmark(s) is targeted by the user. The framework then generates relevant tags for the captured photo using these information. A more versatile sensing framework can be developed using sensors from multiple devices possessed by a user. Sentio is such a framework which enables apps to seamlessly utilize the collective sensing capabilities of the user’s personal devices and of the IoT sensors located in the proximity of the user. With Sentio, an app running on any personal mobile/wearable device can access any sensor of the user in real-time using the same API, can selectively switch to the most suitable sensor of a particular type when multiple sensors of this type are available at different devices, and can build composite sensors. Sentio offers seamless connectivity to sensors even if the sensor-accessing code is offloaded to the cloud. Sentio provides these functionalities with a high-level API and a distributed middleware that handles all low-level communication and sensor management tasks. This dissertation also proposes Moitree, a middleware for the mobile cloud platforms where each mobile device is augmented by an avatar, a per-user always-on software entity that resides in the cloud. Mobile-avatar pairs participate in distributed computing as a unified computing entity. Moitree provides a common programming and execution framework for mobile distributed apps. Moitree allows the components of a distributed app to execute seamlessly over a set of mobile/avatar pairs, with the provision of offloading computation and communication to the cloud. The programming framework has two key features: user collaborations are modeled using group semantics - groups are created dynamically based on context and are hierarchical; data communication among group members is offloaded to the cloud through high-level communication channels. Finally, this dissertation presents and discusses CASINO, a collaborative sensor-driven computation offloading framework which can be used alongside Moitree. This framework includes a new scheduling algorithm which minimizes the total completion time of a collaborative computation that executes over a set of mobile/avatar pairs. Using the CASINO API, the programmers can mark their classes and functions as ”offloadable”. The framework collects profiling information (network, CPU, battery, etc.) from participating users’ mobile devices and avatars, and then schedules ”offloadable” tasks in mobiles and avatars in a way that reduces the total completion time. The scheduling problem is proven to be NP-Hard and there is no polynomial time optimization algorithm for it. The proposed algorithm can generate a schedule in polynomial time using a topological sorting and greedy technique

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

Using mobile devices as scientific measurement instruments: Reliable android task scheduling

In various usage scenarios, smartphones are used as measuring instruments to systematically and unobtrusively collect data measurements (e.g., sensor data, user activity, phone usage data). Unfortunately, in the race towards extending battery life and improving privacy, mobile phone manufacturers are gradually restricting developers in (frequently) scheduling background (sensing) tasks and impede the exact scheduling of their execution time (i.e., Android’s “best effort” approach). This evolution hampers successful deployment of smartphones in sensing applications in scientific contexts, with unreliable and incomplete sampling rates frequently reported in literature. In this article, we discuss the ins and outs of Android’s background tasks scheduling mechanism, and formulate guidelines for developers to successfully implement reliable task scheduling. Implementing these guidelines, we present a software library, agnostic from the underlying Android scheduling mechanisms and restrictions, that allows Android developers to reliably schedule tasks with a maximum sampling rate of one minute. Our evaluation demonstrates the use and versatility of our task scheduler, and experimentally confirms its reliability and acceptable energy usage.Funding for open access charge: CRUE-Universitat Jaume

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making