Using Vital Sensors in Mobile Healthcare Business Applications: Challenges, Examples, Lessons Learned

Today, sensors are increasingly used for data collection. In the medical domain, for example, vital signs (e.g., pulse or oxygen saturation) of patients can be measured with sensors and used for further processing. In this paper, different types of applications will be discussed whether sensors might be used in the context of these applications and their suitability for applying external sensors to them. Furthermore, a system architecture for adding sensor technology to respective applications is presented. For this purpose, a real-world business application scenario in the field of well-being and fitness is presented. In particular, we integrated two different sensors in our fitness application. We report on the lessons learned from the implementation and use of this application, e.g., in respect to connection and data structure. They mainly deal with problems relating to the connection and communication between the smart mobile device and the external sensors, as well as the selection of the appropriate type of application. Finally, a robust sensor framework, arising from this fitness application is presented. This framework provides basic features for connecting sensors. Particularly, in the medical domain, it is crucial to provide an easy to use toolset to relieve medical staff

Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

BPM to Go: Supporting Business Processes in a Mobile and Sensing World

The growing maturity of smart mobile devices has fostered their prevalence in a multitude of business areas. As a consequence, business process management (BPM) technologies need to be enhanced with sophisticated and configurable mobile task support. Along characteristic use cases from different application domains (e.g., healthcare and logistics), this chapter will give insights into the challenges, concepts and technologies relevant for integrating mobile task support with business processes. Amongst others, we will show how mobile task support can be enhanced with location-based data, sensor integration, and mobile task configuration support. The latter is based on a 3D model for configuring mobile tasks on smart mobile devices

A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

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

Smartphone as an Edge for Context-Aware Real-Time Processing for Personal e-Health

The medical domain is facing an ongoing challenge of how patients can share their health information and timeline with healthcare providers. This involves secure sharing, diverse data types, and formats reported by healthcare-related devices. A multilayer framework can address these challenges in the context of the Internet of Medical Things (IoMT). This framework utilizes smartphone sensors, external services, and medical devices that measure vital signs and communicate such real-time data with smartphones. The smartphone serves as an “edge device” to visualize, analyze, store, and report context- aware data to the cloud layer. Focusing on medical device connectivity, mobile security, data collection, and interoperability for frictionless data processing allows for building context-aware personal medical records (PMRs). These PMRs are then securely transmitted through a communication protocol, Message Queuing Telemetry Transport (MQTT), to be then utilized by authorized medical staff and healthcare institutions. MQTT is a lightweight, intuitive, and easy-to-use messaging protocol suitable for IoMT systems. Consequently, these PMRs are to be further processed in a cloud computing platform, Amazon Web Services (AWS). Through AWS and its services, architecting a customized data pipeline from the mobile user to the cloud allows displaying of useful analytics to healthcare stakeholders, secure storage, and SMS notifications. Our results demonstrate that this framework preserves the patient’s health-related timeline and shares this information with professionals. Through a serverless Business intelligence interactive dashboard generated from AWS QuickSight, further querying and data filtering techniques are applied to the PMRs which identify key metrics and trends

Process-Driven Data Collection with Smart Mobile Devices

Paper-based questionnaires are often used for collecting data in application domains like healthcare, psychology or education. Such paper-based approach, however, results in a massive workload for processing and analyzing the collected data. In order to relieve domain experts from these manual tasks, we propose a process-driven approach for implementing as well as running respective mobile business applications. In particular, the logic of a questionnaire is described in terms of an explicit process model. Based on this process model, in turn, multiple questionnaire instances may be created and enacted by a process engine. For this purpose, we present a generic architecture and demonstrate the development of electronic questionnaires in the context of scientific studies. Further, we discuss the major challenges and lessons learned. In this context the presented process-driven approach offers promising perspectives in respect to the development of mobile data collection applications