Enhacing the Interoperability Aspect of Formal and Informal Healthcare Systems
Darmstadt, TU, Master Thesis, 2018
Increasing number of mobile devices (Personal Health Devices) are used at homes for first hand knowledge of vital signs like Blood pressure, glucose level, weight, temperature. The need to access this data directly by care givers (clinicians, doctors, hospitals) is becoming a norm. Personal Health devices (PHDs) can now be purchased at prices affordable to vast majority of people and ability to use them without any hindrance is reducing due to increase in focus on simple and intuitive user interface of these devices. It is estimated that a 300 million people will enter old age by 2025  and the need for affordable health care for patients and care givers at their fingertips will be the need of the hour. By collaborating with other ecosystems like smart home the number of use cases increases and will provide more incentive for general public to buy into the ecosystem. In this thesis functionality of the existing application, Smart Doktor enhanced by building a formal interoperable approach adhering to HL7 standard messaging and transport mechanisms. We extend the Smart Doktor application with two new vital signs namely ECG and PPG along with Smart devices namely, Smart Movement sensor and Smart Bed which provides a new insights into the patient under treatment and provides care givers ability to enhance the care they provide. An open source real dataset is used for implementing these devices using open source integration software tool “HL7Soup“.
Smart Recommendation Systems for IoT
Darmstadt, TU, Master Thesis, 2018
Nowadays Internet of Things is a important research field in Computer Science. The personalization of IoT services is important for the usability and user experience, but the rapidly growing number of IoT services and therefore the increasing number of possibilities, makes it hard for the user to configure an IoT system. Recommender systems are there to automate the decision process. But a lot of different recommender algorithms exist and it is hard to decide for a certain method regarding a specific IoT Use Case. In this master thesis recommender system methods are investigated in regard to their characteristics as well as IoT properties and scenarios. The goal of this research is to suggest a generic model for selecting and executing recommender system methods depending on a given IoT scenario. All defined tasks were accomplished successfully. A complete concept was created and realized as an web interface application, which was evaluated with data of two IoT use cases.
Enabling an Internet of Things Framework for Ambient Assisted Living
Ambient Assisted Living
Ambient Assisted Living (AAL) <9, 2016, Frankfurt, Germany>
Ambient Assisted Living (AAL) technologies hold great potential to meet the challenges of health, support, comfort and social services in European countries. After years of research, innovation and development in the field of health care and life support, there is still a lack of good practices on how to improve the market uptake of AAL solutions, how to commercialize laboratory results and prototypes and achieve widely accepted mature solutions with a significant footprint in the European market. The Internet of Things (IoT) consists of Internet connected objects such as sensors and actuators, as well as Smart appliances. Due to its characteristics, requirement and impact on real life system, the IoT has gained significant attention over the last few years. The major goal of this paper is to strategically specify and demonstrate the impact of the usage of IoT technology and the respect of IoT specification on the quality and future collaborative usage and extendability of deployed AAL solutions in real life.
Enhancing User Experience in Internet of Things Systems in terms of Smart Rule Management
Darmstadt, TU, Master Thesis, 2017
Enhancing User Experience with respect to Device Management in Internet of Things Domain
Darmstadt, TU, Master Thesis, 2017
There has been a steady increase in the use and development of the IoT devices. This is due to the increasing capabilities of the IoT devices and companies desire to provide better and easy services to the IoT users. An IoT system consists of multiple devices, which work, in cooperation to provide different services to the users. Since the numbers of IoT devices are increasing in the system, it is becoming difficult to manage all the IoT devices in the existing system. Consider the example of a Home IoT System, which consists of hundreds of devices, and sensors, which work in collaboration to provide the services. The management of large number of devices for a novice end user will be very challenging and complex task. The primary focus of this thesis is simplifying and reducing the complexity of managing the IoT devices with perspective of the novice end users, thus enhancing the overall experience for the end users.
New Approach for Optimizing the Usage of Situation Recognition Algorithms Within IoT Domains
European Conference on Ambient Intelligence (AmI) <13, 2017, Malaga, Spain>
The growth of the Internet of Things (IoT) over the past few years enabled a lot of application domains. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the continuously running situation recognition algorithms. To overcome these problems, this paper proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received, thus allowing the reduction of the situation recognition algorithms execution frequency and saving computational resources.
New Approach for Optimizing Usage of Situation Recognition Algorithms within IoT Domains
Darmstadt, TU, Master Thesis, 2016
Over the past few years technological advancements have supported the growth of the Internet of Things (IoT). The Internet of Things consists of (smart) objects embedded with sensors, actuators and controllers. These objects are connected to the Internet and are able to communicate with each other. The interconnection and communication of objects enable the creation of different application domains within the Internet of Things. Smart living is one of the major application areas for the Internet of Things. Sensors, actuators and controllers in a smart living environment (e.g. smart homes) are deployed anywhere; on objects or even on persons. As sensors have the capability to sense the environment, they can be used to collect useful information on location, motion, temperature, humidity, light, etc. Actuators can perform different actions based on data gathered from sensors, and controllers can process that data. Real-time situation awareness is one of the key tasks in a smart living environment. Real-time recognition of situations is especially important in ambient assisted living environments, where elderly or disabled people need support in their everyday lives. Recognition of situations in real-time enables immediate identification of critical situations and provides just-in-time assistance. To detect situations, data needs to be monitored, collected, analyzed and processed. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the inefficiency of continuously-running pattern/situation recognition algorithms, high requirement for processing capability and high frequency of the recognition process. Situation recognition algorithms must be executed constantly to handle the continuously generated data. For real-time recognition of situations in particular, these algorithms need to be executed permanently for all received data. The continuously-running recognition algorithms require high processing capabilities. The resource consumption of these algorithms is especially high when they are running on large sets of data. To overcome these problems there is a need for more intelligent approaches that are able to decide - based on target situation recognition purposes - which data is important and should be processed and which algorithm should be used to process this data. This study proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received. This allows reduction of the frequency of execution of the situation recognition algorithms, thus saving computational resources, such as CPU, memory, storage, bandwidth and power. Another advantage of our approach is that it can be applied to recognize situations in real-time, which is useful for ambient assisted living environments. We apply the proposed approach to implement a use case scenario on top of the universAAL IoT platform, which is an open-source platform for the development of IoT solutions.
Scaling up IoT: Impact of Semantic Open Platforms
VDE-Kongress 2016 - Internet der Dinge
VDE-Kongress <2016, Mannheim>
The Internet of Things (IoT) consists of connected objects such as sensors and actuators, as well as smart services. Due to its characteristics, requirements and impact on real life system, the IoT has gained significant attention over the last few years. The main reported issue is the exponential growing number of "Things". Among the open platform technologies, a semantic open platform offers the opportunity to reduce the system complexity, ensuring a direct communication between heterogeneous components without knowing each other, and sharing data based on a "common" semantic model without any need for a specific API. The major goal of this contribution is to clarify the previously highlighted advantages and to strategically recommend the usage of semantic open platforms thus facilitating the growth of the IoT.
Towards the Deployment of Open Platform AAL Services in Real Life-advantages and Lessons Learned
ICT 4 AgeingWell 2015
International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell) <1, 2015, Lisbon, Portugal)
Nowadays, most Ambient Assisted Living systems are confined to individual projects. They are primarily closed products with a limited set of features, thus reducing its extension, adoption and reuse. The aim of this paper is to make a first attempt to increase standardization and interoperability oriented efforts by focusing on open systems. We aim to share our experience with developing and deploying Ambient Assisted Living solutions on the top level of the standardized open platform universAAL in real life. This paper identifies the essential aspects of the system architecture and investigates the advantages of providing generic services, shared and reusable components in real life. In addition, this paper presents an evaluation protocol of the different components, focusing on system sustainability and reliability.