Living lab approach for developing massmarket IoT products and services

Internet of Things (IoT) has emerged as a central concept in both the industrial as in the academic world. In this context, Living Lab research has been shown as an effective means for the design, implementation, development, testing and validation of Internet of Things system’s pervasiveness. However, IoT products are not yet designed based on the needs of a larger, non-technical group of end-users. Therefore, in this paper we describe the AllThingsTalk Living Lab research track in which tangible end-user products are defined to be implemented on an online IoT platform. More specifically, by using both qualitative and quantitative methodologies (i.e., desk research, online survey, probe research and co-creation) and by selecting different types of users (i.e., based on Rogers’ adoption profiles) for these interaction moments, we were able to combine the input of these users to define tangible products that meet the needs of a heterogeneous group of end-users

Connecting with citizen journalists: an exploratory Living lab study on motivations for using mobile reporting applications

In the context of user generated content in the newsroom, mobile reporting applications are seen as a facilitator of citizen journalism, bringing news items from the user to the newsroom and vice versa. In this paper, we describe a Living Lab project aimed at developing a mobile reporting application for a regional television broadcaster that struggles reaching a young audience. Data were collected through an online survey (n:500), one focus group (n:9), a field trial (n:35) and in-depth interviews (n:10). Besides user motivations for using mobile reporting applications, we provide three user profiles and give insights in citizen journalism projects. A mobile reporting application could provide a solution for regional news stations to reach more youngsters, however, reasonable efforts should be taken to make such a project succeed

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

Living labs for in-situ open innovation: from idea to product validation and beyond

In this paper we present the Living Lab methodology as an overall framework for in-situ open innovation involving the end-user as equal participant in the innovation process. As a specific form of distributed innovation, relying on co-creation, we demonstrate the applicability of the Living Lab-approach for home ICT innovation by means of four innovation projects in different stages of maturity. We describe the used research methodologies and reflect on the role of the user

Co-creation in living labs: exploring the role of user characteristics on innovation contribution

Since the 1970s, the innovative potential of users has been recognized by von Hippel and his seminal works on the Customer Active Paradigm (CAP) and Lead Users. This fostered further research into the nature of user contribution in NPD and the characteristics of innovative and innovating users. This research stream has been labeled user innovation and looks at the utility gains for end-users when involved in innovation. More recently, open innovation approaches have been looking to integrate the insights and creative potential of users through various methods and tools. One of these approaches gaining ground are the so-called Living Labs, an innovation approach relying on intensive user involvement through co-creation, using real-life settings and a multi-stakeholder approach. Although user involvement is seen as key within these Living Labs, research integrating the insights from user innovation into ways of user selection and user contribution in Living Labs is scarce. Within this paper, we will explore some of the hypotheses from user innovation regarding user characteristics in three concrete Living Lab projects and assess whether these characteristics have an impact on the outcomes and on the user contribution. The results indicate that it is necessary to take into account domain-related as well as innovation-specific characteristics, otherwise this may lead to one-dimensional user contributions. Moreover, our research suggests that Living Labs are capable to facilitate a diversity of user contributions through a mix of self-selection and purposeful sampling

rosettR: protocol and software for seedling area and growth analysis

Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments

Development of EULAR recommendations for the reporting of clinical trial extension studies in rheumatology

Objectives: Our initiative aimed to produce recommendations on post-randomised controlled trial (RCT) trial extension studies (TES) reporting using European League Against Rheumatism (EULAR) standard operating procedures in order to achieve more meaningful output and standardisation of reports. Methods: We formed a task force of 22 participants comprising RCT experts, clinical epidemiologists and patient representatives. A two-stage Delphi survey was conducted to discuss the domains of evaluation of a TES and definitions. A ‘0–10’ agreement scale assessed each domain and definition. The resulting set of recommendations was further refined and a final vote taken for task force acceptance. Results: Seven key domains and individual components were evaluated and led to agreed recommendations including definition of a TES (100% agreement), minimal data necessary (100% agreement), method of data analysis (agreement mean (SD) scores ranging between 7.9 (0.84) and 9.0 (2.16)) and reporting of results as well as ethical issues. Key recommendations included reporting of absolute numbers at each stage from the RCT to TES with reasons given for drop-out at each stage, and inclusion of a flowchart detailing change in numbers at each stage and focus (mean (SD) agreement 9.9 (0.36)). A final vote accepted the set of recommendations. Conclusions: This EULAR task force provides recommendations for implementation in future TES to ensure a standardised approach to reporting. Use of this document should provide the rheumatology community with a more accurate and meaningful output from future TES, enabling better understanding and more confident application in clinical practice towards improving patient outcomes

Reporting and Methods in Clinical Prediction Research: A Systematic Review

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations

Use-and QoE-related aspects of personal cloud applications: an exploratory survey

Although Quality of Experience (QoE) is pushed forward as a crucial concept in the context of the migration of services to the cloud, only a few studies so far have investigated cloud QoE from a users’ point of view. This paper shares insights from an exploratory study on use- and QoE-related aspects of personal cloud services and applications. More concretely, we conducted an online survey (N= 349) among users of personal cloud services and applications in order to gain a better understanding of how and why they are used and to detect possible QoE influencing factors and relevant features. Availability, accessibility, and compatibility are considered to be of crucial importance. The same goes for cost, privacy and security related aspects. Use also varies across different contexts and associated network conditions