Towards a user-centered development process of machine learning applications for manufacturing domain experts

2020 ◽  
Author(s):  
Akos Csiszar ◽  
Philipp Hein ◽  
Michael Wachter ◽  
Alexander Verl ◽  
Angelika C. Bullinger
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
Domain Experts ◽  
Machine Learning Applications ◽  
User Centered Development ◽  
User Centered

scholarly journals The Interactive Urban Robot: User-centered development and final field trial of a direction requesting robot

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Astrid Weiss ◽  
Nicole Mirnig ◽  
Ulrike Bruckenberger ◽  
Ewald Strasser ◽  
Manfred Tscheligi ◽  
...  
Keyword(s):  
Development Process ◽  
Service Robot ◽  
Communication Model ◽  
City Center ◽  
Secondary Users ◽  
Feedback Modalities ◽  
Socially Interactive Robots ◽  
The City ◽  
User Centered Development ◽  
User Centered

AbstractIn this article, we present the user-centered development of the service robot IURO. IURO’s goal is to find the way to a designated place in town without any previous map knowledge, just by retrieving information from asking pedestrians for directions. We present the 3-years development process,which involved a series of studies on its appearance, communication model, feedback modalities, and social navigation mechanisms. Our main contribution lies within the final field trial.With the autonomous IURO platform, we performed a series of six way-finding runs (over 24 hours of run-time in total) in the city center of Munich, Germany. The robot interacted with approximately 100 pedestrians of which 36 interactions included a full route dialogue. A variety of empirical methods was used to explore reactions of primary users (pedestrians who actually interacted with the robot) and secondary users (bystanders who observed others interacting). The gathered data provides insights into usability, user experience, and acceptance of IURO and allowed us deriving recommendations for the development of other socially interactive robots.

scholarly journals A Survey of Domain Knowledge Elicitation in Applied Machine Learning

2021 ◽  
Vol 5 (12) ◽  
pp. 73
Author(s):  
Daniel Kerrigan ◽  
Jessica Hullman ◽  
Enrico Bertini
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Development Process ◽  
Model Development ◽  
Knowledge Elicitation ◽  
Future Directions ◽  
Domain Experts ◽  
Applied Machine Learning ◽  
Elicitation Process ◽  
Model Development Process

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.

Usability and User Experience Evaluation Methods

2010 ◽  
pp. 232-243 ◽  
Author(s):  
Regina Bernhaupt
Keyword(s):  
User Experience ◽  
Development Process ◽  
Evaluation Methods ◽  
Interactive Tv ◽  
Interaction Technique ◽  
Set Up ◽  
User Centered Development ◽  
User Centered

Usability and user experience are two important factors in the development of mass-customizable personalized products. A broad range of evaluation methods is available to improve products during an user-centered development process. This chapter gives an overview on these methods and how to apply them to achieve easy-to-use, efficient and effective personalized products that are additionally fun to use. A case study on the development of a new interaction technique for interactive TV helps to understand how to set up a mix of evaluation methods to cope with some of the limitations of current usability and user experience evaluation methods. The chapter concludes with some guidelines of how to change organizations to focus on usability and user experience.

scholarly journals Benchmark and Survey of Automated Machine Learning Frameworks

2021 ◽  
Vol 70 ◽  
pp. 409-472
Author(s):  
Marc-André Zöller ◽  
Marco F. Huber
Keyword(s):  
Machine Learning ◽  
Daily Life ◽  
Real Data ◽  
Data Sets ◽  
Domain Experts ◽  
Vital Part ◽  
Machine Learning Applications ◽  
Automated Machine Learning ◽  
Learning Frameworks

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Involving people with dementia and their informal caregivers in the development process of assistive technology: Description and evaluation of a user-centered design (Preprint)

2020 ◽  
Author(s):  
Iris A.G.M. Geerts ◽  
Liselore J.A.E Snaphaan ◽  
Inge M.B. Bongers
Keyword(s):  
Focus Groups ◽  
Assistive Technology ◽  
Field Study ◽  
Development Process ◽  
Technology Development ◽  
Informal Caregivers ◽  
Assistive Technologies ◽  
User Centered Design ◽  
People With Dementia ◽  
User Centered

BACKGROUND Despite the potential value of assistive technology to support people with dementia (PWD) in everyday activities, use of these technologies is still limited. To ensure that assistive technologies better address the specific needs and daily context of PWD and their informal caregivers, it is particularly important to involve them in all different phases of assistive technology development. The literature rarely describes the involvement of PWD throughout the development process of assistive technologies, which makes it difficult to further reflect on and improve active involvement of PWD. OBJECTIVE This two-part study aimed to gather insights on the user-centered design (UCD) applied in the development process of the alpha prototype of the serious game PLAYTIME by describing the methods and procedures of the UCD as well as evaluating the UCD from the perspective of all involved stakeholders. METHODS The first three phases of the user-driven Living Lab of Innovate Dementia 2.0 were applied to directly involve PWD and their informal caregivers through qualitative research methods, including focus groups and a context-field study, in the development of the alpha prototype of PLAYTIME from exploration to design to testing. After the testing phase, a total number of 18 semi-structured interviews were conducted with PWD, their informal caregivers and the project members of PLAYTIME to evaluate the applied UCD from the perspective of all involved stakeholders. The interviews addressed five of the principles for successful UCD and the appropriateness of the different methods used in the focus groups and context-field study. RESULTS Results of the interviews focused, amongst others, on the level of involvement of PWD and their informal caregivers in the development process, the input provided by PWD and their informal caregivers, the value of early prototyping, continuous iterations of design solutions and in-context testing, the role of dementia care professionals in the multidisciplinary project team, and the appropriateness of open- and closed-ended questions for obtaining input from PWD and their informal caregivers. CONCLUSIONS The description and evaluation of the UCD applied in the development process of the alpha prototype of PLAYTIME resulted in several insights on the relevance of UCD for all involved stakeholders as well as how PWD can be involved in the subsequent phases of usable and meaningful assistive technology development.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

2021 ◽  
Vol 3 (2) ◽  
pp. 392-413
Author(s):  
Stefan Studer ◽  
Thanh Binh Bui ◽  
Christian Drescher ◽  
Alexander Hanuschkin ◽  
Ludwig Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Practical Experience ◽  
Special Focus ◽  
Close Monitoring ◽  
Machine Learning Applications ◽  
Project Organizations ◽  
Considerable Impact ◽  
Learning Development

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

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