Comprehensive Survey on Automatic Reminder Systems

In today’s world old people, pregnant women’s needs the medicines on daily basis and on exact time. People who suffered with cronical diseases like Diabetics, Dementia, Heart patients need the exact dosage of medicine on right time otherwise it will affect their body. So we need to see such useful reminding system which helps to above people to take their medicine on exact time. It’s Automatic Medicine Reminder System which can be used with latest technologies. Today, GSM Module can be used to send the Text messages for reminding purpose of medicine those who don’t have smart phones, and for those who have Smartphone’s can get same reminding message but with advance feature and its Whatsapp messaging with the help of latest processors like Raspberry Pi module, which can operates through Ethernet or WIFI. This paper summarized the various techniques used for reminder purpose basically medical treatments

현장 데이터 수집 능력을 확장하기 위한 자유도 높은 셀프 트래킹 기술의 디자인

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 서진욱.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change. Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits. The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design. OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields. Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process. This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.일상의 맥락에서 데이터를 모으는 활동인 셀프 트래킹(self-tracking)은 개인과 연구의 영역에서 활발히 활용되고 있다. 웨어러블 디바이스와 모바일 기술의 발달로 인해 사람들은 각자의 삶에 대해 말해주는 데이터를 더 쉽게 수집하고, 통찰할 수 있게 되었다. 또한, 연구자들은 현장(in situ) 데이터를 수집하거나 사람들에게 행동 변화를 일으키는 데에 셀프 트래킹을 활용한다. 비록 셀프 트래킹을 위한 다양한 도구들이 존재하지만, 트래킹에 대해 다양화된 요구와 취향을 완벽히 충족하는 것들을 찾는 것은 쉽지 않다. 대부분의 셀프 트래킹 도구는 이미 설계된 부분을 수정하거나 확장하기에 제한적이다. 그렇기 때문에 사람들의 셀프 트래킹에 대한 자유도는 기존 도구들의 디자인 공간에 의해 제약을 받을 수밖에 없다. 마찬가지로, 현장 데이터를 수집하는 연구자들도 이러한 도구의 한계로 인해 여러 문제에 봉착한다. 연구자들이 데이터를 통해 답하고자 하는 연구 질문(research question)은 분야가 발전할수록 세분되고, 치밀해지기 때문에 이를 위해서는 복잡하고 고유한 실험 설계가 필요하다. 하지만 현존하는 연구용 셀프 트래킹 플랫폼들은 이에 부합하는 자유도를 발휘하지 못한다. 이러한 간극으로 인해 많은 연구자들이 각자의 현장 데이터 수집 연구에 필요한 디지털 도구들을 직접 구현하고 있다. 본 연구의 목표는 자유도 높은---연구적 맥락과 개인적 맥락을 아우르는 다양한 상황에 활용할 수 있는---셀프 트래킹 기술을 디자인하는 것이다. 이를 위해 본고에서는 옴니트랙(OmniTrack)이라는 디자인 접근법을 제안한다. 옴니트랙은 자유도 높은 셀프 트래킹을 위한 방법론이며, 반자동 트래킹(semi-automated tracking)이라는 컨셉을 바탕으로 수동 방식과 자동 방식의 조합을 통해 임의의 트래커를 표현할 수 있다. 먼저 옴니트랙을 개인을 위한 모바일 앱 형태로 구현하였다. 옴니트랙 앱은 개개인이 자신의 트래킹 니즈에 맞는 트래커를 커스터마이징하여 활용할 수 있도록 구성되어 있다. 본고에서는 사람들이 어떻게 옴니트랙을 자신의 니즈에 맞게 활용하는지 알아보고자 사용성 테스트(usability testing)와 필드 배포 연구(field deployment study)를 수행하였다. 참가자들은 옴니트랙을 활발히 이용해 다양한 디자인의 트래커—아주 단순한 감정 트래커부터 여러 개의 필드를 가진 복잡한 일일 활동 트래커까지—들을 생성하고, 수정하고, 활용하였다. 다음으로, 옴니트랙을 현장 데이터 수집 연구에 활용할 수 있도록 연구 플랫폼 형태의 '옴니트랙 리서치 킷(OmniTrack Research Kit)'으로 확장하였다. 옴니트랙 리서치 킷은 연구자들이 프로그래밍 언어 없이 원하는 실험을 설계하고 옴니트랙 앱을 참가자들의 스마트폰으로 배포할 수 있도록 디자인되었다. 그리고 옴니트랙 리서치 킷을 이용해 일지기록 연구(diary study)를 직접 수행하였고, 이를 통해 옴니트랙 접근법이 어떻게 연구자들의 연구 목적을 이루는 데에 도움을 줄 수 있는지 직접 확인하였다. 본 연구는 휴먼-컴퓨터 인터랙션(Human-Computer Interaction) 및 유비쿼터스 컴퓨팅(Ubiquitous Computing) 분야에 기술적 산출물로써 기여하며, 자유도 높은 셀프 트래킹 도구가 어떻게 개인과 연구자들을 도울 수 있는지 실증적인 이해를 증진한다. 또한, 자유도 높은 셀프트래킹 기술에 대한 디자인적 난제, 연구에서 제시한 시스템에 대한 개선방안, 마지막으로 본 연구에서 다루지 못한 다른 집단을 지원하기 위한 향후 연구 논제에 대하여 논의한다.Abstract CHAPTER 1. Introduction 1.1 Background and Motivation 1.2 Research Questions and Approaches 1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated Tracking 1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts 1.2.3 Designing a Research Platform for In Situ Data Collection Studies Leveraging OmniTrack 1.2.4 A Case Study of Conducting an In Situ Data Collection Study using the Research Platform 1.3 Contributions 1.4 Structure of this Dissertation CHAPTER 2. Related Work 2.1 Background on Self-Tracking 2.1.1 Self-Tracking in Personal Tracking Contexts 2.1.2 Utilization of Self-Tracking in Other Contexts 2.2 Barriers Caused by Limited Tool Support 2.2.1 Limited Tools and Siloed Data in Personal Tracking 2.2.2 Challenges of the Instrumentation for In Situ Data Collection 2.3 Flexible Self-Tracking Approaches 2.3.1 Appropriation of Generic Tools 2.3.2 Universal Tracking Systems for Individuals 2.3.3 Research Frameworks for In Situ Data Collection 2.4 Grounding Design Approach: Semi-Automated Tracking 2.5 Summary of Related Work CHAPTER3 DesigningOmniTrack: a Flexible Self-Tracking Approach 3.1 Design Goals and Rationales 3.2 System Design and User Interfaces 3.2.1 Trackers: Enabling Flexible Data Inputs 3.2.2 Services: Integrating External Trackers and Other Services 3.2.3 Triggers: Retrieving Values Automatically 3.2.4 Streamlining Tracking and Lowering the User Burden 3.2.5 Visualization and Feedback 3.3 OmniTrack Use Cases 3.3.1 Tracker 1: Beer Tracker 3.3.2 Tracker 2: SleepTight++ 3.3.3 Tracker 3: Comparison of Automated Trackers 3.4 Summary CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack 4.1 Usability Study 4.1.1 Participants 4.1.2 Procedure and Study Setup 4.1.3 Tasks 4.1.4 Results and Discussion 4.1.5 Improvements A_er the Usability Study 4.2 Field Deployment Study 4.2.1 Study Setup 4.2.2 Participants 4.2.3 Data Analysis and Results 4.2.4 Reflections on the Deployment Study 4.3 Discussion 4.3.1 Expanding the Design Space for Self-Tracking 4.3.2 Leveraging Other Building Blocks of Self-Tracking 4.3.3 Sharing Trackers with Other People 4.3.4 Studying with a Broader Audience 4.4 Summary CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection Studies 5.1 Design Space of Study Instrumentation for In-Situ Data Collection 5.1.1 Experiment-Level Dimensions 5.1.2 Condition-Level Dimensions 5.1.3 Tracker-Level Dimensions 5.1.4 Reminder/Trigger-Level Dimensions 5.1.5 Extending OmniTrack to Cover the Design Space 5.2 Design Goals and Rationales 5.3 System Design and User Interfaces 5.3.1 Experiment Management and Collaboration 5.3.2 Experiment-level Configurations 5.3.3 A Participants Protocol for Joining the Experiment 5.3.4 Implementation 5.4 Replicated Study Examples 5.4.1 Example A: Revisiting the Deployment Study of OmniTrack 5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food Logger 5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement on Habit Formation 5.4.4 Example D: Collecting Stress and Activity Data for Building a Prediction Model 5.5 Discussion 5.5.1 Supporting Multiphase Experimental Design 5.5.2 Serving as Testbeds for Self-Tracking Interventions 5.5.3 Exploiting the Interaction Logs 5.6 Summary CHAPTER 6. Using the OmniTrack Research Kit: A Case Study 6.1 Study Background and Motivation 6.2 OmniTrack Configuration for Study Instruments 6.3 Participants 6.4 Study Procedure 6.5 Dataset and Analysis 6.6 Study Result 6.6.1 Diary Entries 6.6.2 Aspects of Productivity Evaluation 6.6.3 Productive Activities 6.7 Experimenter Experience of OmniTrack 6.8 Participant Experience of OmniTrack 6.9 Implications 6.9.1 Visualization Support for Progressive, Preliminary Analysis of Collected Data 6.9.2 Inspection to Prevent Misconfiguration 6.9.3 Providing More Alternative Methods to Capture Data 6.10 Summary CHAPTER 7. Discussion 7.1 Lessons Learned 7.2 Design Challenges and Implications 7.2.1 Making the Flexibility Learnable 7.2.2 Additive vs. Subtractive Design for Flexibility 7.3 Future Opportunities for Improvement 7.3.1 Utilizing External Information and Contexts 7.3.2 Providing Flexible Visual Feedback 7.4 Expanding Audiences of OmniTrack 7.4.1 Supporting Clinical Contexts 7.4.2 Supporting Self-Experimenters 7.5 Limitations CHAPTER 8. Conclusion 8.1 Summary of the Approaches 8.2 Summary of Contributions 8.2.1 Artifact Contributions 8.2.2 Empirical Research Contributions 8.3 Future Work 8.3.1 Understanding the Long-term E_ect of OmniTrack 8.3.2 Utilizing External Information and Contexts 8.3.3 Extending the Input Modality to Lower the Capture Burden 8.3.4 Customizable Visual Feedback 8.3.5 Community-Driven Tracker Sharing 8.3.6 Supporting Multiphase Study Design 8.4 Final Remarks APPENDIX A. Study Material for Evaluations of the OmniTrack App A.1 Task Instructions for Usability Study A.2 The SUS (System Usability Scale) Questionnaire A.3 Screening Questionnaire for Deployment Study A.4 Exit Interview Guide for Deployment Study A.5 Deployment Participant Information APPENDIX B Study Material for Productivity Diary Study B.1 Recruitment Screening Questionnaire B.2 Exit Interview Guide Abstract (Korean)Docto

A framework for safe composition of heterogeneous SOA services in a pervasive computing environment with resource constraints

The Service Oriented Computing (SOC) paradigm, defines services as software artifacts whose implementations are separated from their specifications. Application developers rely on services to simplify the design, reduce the development time and cost. Within the SOC paradigm, different Service Oriented Architectures (SOAs) have been developed. These different SOAs provide platform independence, programming-language independence, defined standards, and network support. Even when different SOAs follow the same SOC principles, in practice it is difficult to compose services from heterogeneous architectures. Automatic the process of composition of services from heterogeneous SOAs is not a trivial task. Current composition tools usually focus on a single SOA, while others do not provide mechanisms for ensuring safety of composite services and their interactions. Given that some services might perform critical operations or manage sensitive data, defining safety for services and checking for compliance is crucial. This work proposes and workflow specification language for composite services that is SOA-independent. It also presents a framework for automatic composition of services of heterogeneous SOAs, supporting web services (WS) and OSGi services as an example. It integrates formal software analysis methods to ensure the safety of composite services and their interactions. Experiments are conducted to study the performance of the composite service generated automatically by the framework with composite services using current composition methods. We use as an example a smart home composite service for the management of medicines, deployed in a regular and in a resource-constrained network environment

Holistic System Design for Distributed National eHealth Services

publishedVersio

Using quantified-self for future remote health monitoring

Remote monitoring is an essential part of future mHealth systems for the delivery of personal and pervasive healthcare, especially to allow the collection of personal bio-data outside clinical environments. mHealth involves the use of mobile technologies including sensors and smart phones with Internet connectivity to collect personal bio-data. Yet, by its very nature, it presents considerable challenges: (1) it will be a highly distributed task, (2) requiring collection of bio-data from a myriad of sources, (3) to be gathered at the clinical site, (4) and via secure communication channels. To address these challenges, we propose the use of an online social network (OSN) based on the quantified-self, i.e. the use of wearable sensors to monitor, collect and distribute personal bio-data, as a key component of a near-future remote health monitoring system. Additionally, the use of a social media context allows existing social interactions within the healthcare regime to be modeled within a carer network, working in harmony with, and providing support for, existing relationships and interactions between patients and healthcare professionals. We focus on the use of an online social media platform (OSMP) to enable two primitive functions of quantified-self which we consider essential for mHealth, and on which larger personal healthcare services could be built: remote health monitoring of personal bio-data, and an alert system for asynchronous notifications. We analyse the general requirements in a carer network for these two primitive functions, in terms of four different viewpoints within the carer network: the patient, the doctor in charge, a professional carer, and a family member (or friend) of the patient. We propose that a wellbeing remote monitoring scenario can act as a suitable proxy for mHealth monitoring by the use of an OSN. To allow rapid design, experimentation and evaluation of mHealth systems, we describe our experience of creating an mHealth system based on a wellbeing scenario, exploiting the quantified-self approach of measurement and monitoring. The use of wellbeing data in this manner is particularly valuable to researchers and systems developers, as key development work can be completed within a realistic scenario, but without risk to sensitive patient medical data. We discuss the suitability of using wellbeing monitoring as a proxy for mHealth monitoring with OSMPs in terms of functionality, performance and the key challenge in ensuring appropriate levels of security and privacy. We find that OSMPs based on quantified-self offer great potential for enabling personal and pervasive healthcare in an mHealth scenario

Internet of Things (IoT) for Automated and Smart Applications

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications

The few touch digital diabetes diary : user-involved design of mobile self-help tools for peoplewith diabetes

Paper number 2, 4, 5 and 7 are not available in Munin, due to publishers' restrictions: 2. Årsand E, and Demiris G.: "User-Centered Methods for Designing Patient-Centric Self-Help Tools", Informatics for Health and Social Care, 2008 Vol. 33, No. 3, Pages 158-169 (Informa Healthcare). Available at http://dx.doi.org/10.1080/17538150802457562 4. Årsand E, Olsen OA, Varmedal R, Mortensen W, and Hartvigsen G.: "A System for Monitoring Physical Activity Data Among People with Type 2 Diabetes", pages 173-178 in S.K. Andersen, et.al. (eds.) "eHealth Beyond the Horizon - Get IT There", Proceedings of MIE2008, Studies in Health Technology and Informatics, Volume 136, May 2008, ISBN: 978-1-58603-864-9 5. Årsand E, Tufano JT, Ralston J, and Hjortdahl P.: "Designing Mobile Dietary Management Support Technologies for People with Diabetes", Journal of Telemedicine and Telecare, 2008 Volume 14, Number 7, Pp. 329-332 (Royal Society of Medicine Press). Available at http://dx.doi.org/10.1258/jtt.2008.007001 7. Årsand E, Walseth OA, Andersson N, Fernando R, Granberg O, Bellika JG, and Hartvigsen G.: "Using Blood Glucose Data as an Indicator for Epidemic Disease Outbreaks", pages 199-204 in R. Engelbrecht et.al. (eds.): "Connecting Medical Informatics and Bio-Informatics", Proceedings of MIE2005, Studies in Health Technology and Informatics, Volume 116, August 2005, ISBN: 978-1-58603-549-5. Check availabilityParadoxically, the technological revolution that has created a vast health problem due to a drastic change in lifestyle also holds great potential for individuals to take better care of their own health. The first consequence is not addressed in this dissertation, but the second represents the focus of the work presented, namely utilizing ICT to support self-management of individual health challenges. As long as only 35% of the patients in Norway achieve the International Diabetes Federation‟s goal for blood glucose (HbA1c), actions and activities to improve blood glucose control and related factors are needed. The presented work focuses on the development and integration of alternative sensor systems for blood glucose and physical activity, and a fast and effortless method for recording food habits. Various user-interface concepts running on a mobile terminal constitute a digital diabetes diary, and the total concept is referred to as the “Few Touch application”. The overall aim of this PhD project is to generate knowledge about how a mobile tool can be designed for supporting lifestyle changes among people with diabetes. Applying technologies and methods from the informatics field has contributed to improved insight into this issue. Conversely, addressing the concrete use cases for people with diabetes has resulted in the achievement of ICT designs that have been appreciated by the cohorts involved. Cooperation with three different groups of patients with diabetes over several years and various methods and theories founded in computer science, medical informatics, and telemedicine have been combined in design and research on patient-oriented aids. The blood glucose Bluetooth adapter, the step counter, and the nutrition habit registration system that have been developed were all novel and to my knowledge unique designs at the time they were first tested, and this still applies to the latter two. Whether it can be claimed that the total concept presented, the Few Touch application, will increase quality of life, is up to future research and large-scale tests of the system to answer. However, results from the Type 2 diabetes half-year study showed that several of the participants did adjust their medication, food habits and/or physical activity due to use of the application

The Design and Use of a Smartphone Data Collection Tool and Accompanying Configuration Language

Understanding human behaviour is key to understanding the spread of epidemics, habit dispersion, and the efficacy of health interventions. Investigation into the patterns of and drivers for human behaviour has often been facilitated by paper tools such as surveys, journals, and diaries. These tools have drawbacks in that they can be forgotten, go unfilled, and depend on often unreliable human memories. Researcher-driven data collection mechanisms, such as interviews and direct observation, alleviate some of these problems while introducing others, such as bias and observer effects. In response to this, technological means such as special-purpose data collection hardware, wireless sensor networks, and apps for smart devices have been built to collect behavioural data. These technologies further reduce the problems experienced by more traditional behavioural research tools, but often experience problems of reliability, generality, extensibility, and ease of configuration. This document details the construction of a smartphone-based app designed to collect data on human behaviour such that the difficulties of traditional tools are alleviated while still addressing the problems faced by modern supplemental technology. I describe the app's main data collection engine and its construction, architecture, reliability, generality, and extensibility, as well as the programming language developed to configure it and its feature set. To demonstrate the utility of the tool and its configuration language, I describe how they have been used to collect data in the field. Specifically, eleven case studies are presented in which the tool's architecture, flexibility, generality, extensibility, modularity, and ease of configuration have been exploited to facilitate a variety of behavioural monitoring endeavours. I further explain how the engine performs data collection, the major abstractions it employs, how its design and the development techniques used ensure ongoing reliability, and how the engine and its configuration language could be extended in the future to facilitate a greater range of experiments that require behavioural data to be collected. Finally, features and modules of the engine's encompassing system, iEpi, are presented that have not otherwise been documented to give the reader an understanding of where the work fits into the larger data collection and processing endeavour that spawned it