Human-Technology Interaction: Shaping the Future of Industrial User Interfaces

Contents
Contributors
1: Human-Technology Interaction in the Context of Industry 4.0: Current Trends and Challenges
	1.1 Introduction
	1.2 Toward Industry 4.0
	1.3 Human-Technology Interaction Perspectives on Industry 4.0
		1.3.1 Technical Innovations
			1.3.1.1 Touch Interfaces
			1.3.1.2 Natural User Interfaces (NUI)
			1.3.1.3 Extended Reality (XR) User Interfaces
		1.3.2 Application Areas
			1.3.2.1 Manufacturing
			1.3.2.2 Logistics
			1.3.2.3 Maintenance and Repair
			1.3.2.4 Training
	1.4 Research Challenges and Contributions in this Collection
		1.4.1 How Can Assistance Systems Be Implemented and Integrated into the Work Process?
		1.4.2 How Can XR Technology Support Future Work?
		1.4.3 Will Work Become more Human-Centered due to New Technology?
		1.4.4 How Can Technology Acceptance and Trust Within Industry 4.0 Systems Be Achieved?
	1.5 Conclusion
	References
2: Digital Assembly Assistance Systems: Methods, Technologies and Implementation Strategies
	2.1 Background
	2.2 Strategies for the Successful Implementation and Institutionalization of Digital Assistance and Learning Systems
		2.2.1 Objectives of a Successful Implementation Process
		2.2.2 Challenges in the Implementation Process
		2.2.3 Strategies for Implementation and Institutionalization
			2.2.3.1 Overview: Participatory Design Strategies
			2.2.3.2 Process Design Phases
			2.2.3.3 Guiding Questions for Self-Review and Contextual Review
			2.2.3.4 Guiding Questions for Process Design
	2.3 Human Factors Design of the Technological Solution and the Adjacent Work Processes
		2.3.1 Individual and Organizational Parameters
		2.3.2 Technological and Educational Design Dimensions
		2.3.3 Using the Methodology
		2.3.4 Designing Digital Assistance Systems Conducive to Learning
	2.4 Implementing Innovative Assistance, Inspection and Learning Technologies
		2.4.1 HCI Technologies for User and Context Awareness
			2.4.1.1 Data Acquisition Pipeline for Contextually Relevant Information
			2.4.1.2 Complex Event Processing as Central Building Block
		2.4.2 HCI for Ergonomic Assistance
			2.4.2.1 Method of Detecting Poor Ergonomic Posture
				Working Zone
				Working Posture
				Working Angle
				Working Position
			2.4.2.2 Ergonomic Feedback for Employees
		2.4.3 HCI for Quality Assurance
			2.4.3.1 Current Clamping System Assembly Situation
			2.4.3.2 Clamping System Assembly Solution
			2.4.3.3 AR Technology as the Outcome of Systematic Technology Selection
			2.4.3.4 Systems Design and Use
			2.4.3.5 Findings
	2.5 Conclusion and Outlook
		2.5.1 Conclusion
		2.5.2 Outlook
	References
3: Cognitive Operator Support in the Manufacturing Industry - Three Tools to Help SMEs Select, Test and Evaluate Operator Supp...
	3.1 Introduction: Outline of the Chapter
	3.2 Industry 4.0 and the Augmented Worker
		3.2.1 Developments Leading to an Interest in Cognitive Operator Support
			3.2.1.1 Zero Defect and First Time Right for High-Mix Low-Volume and High-Complexity Manufacturing
			3.2.1.2 Travel Restrictions from COVID-19 Pandemic
			3.2.1.3 Employment: Personnel Shortages and Inclusiveness
			3.2.1.4 SME´s Technology Position
	3.3 Operator Support (OS) Canvas Workshop as a Selection Guide
		3.3.1 OS-Canvas in Short
		3.3.2 Technology: What Kind of Technologies Are Available?
		3.3.3 Filling in the Canvas
			3.3.3.1 Goal: Why Implement a New Way of Providing Work Instructions?
			3.3.3.2 Target Group: Who Is It for?
			3.3.3.3 Process in Scope: Which Process Steps Are Reviewed in the Canvas Session?
			3.3.3.4 Process Description: What Are the Process Steps?
			3.3.3.5 Information Needs: What Is Needed for Comfortable, Fast and Zero-Defect Process Execution?
			3.3.3.6 Context: What Requirements Come from the Context?
			3.3.3.7 Report: Canvas Summary and Short Business Case Analysis
		3.3.4 Use Case Descriptions: Canvas Examples from Two Use Cases
			3.3.4.1 Company A: Shipment Assembly
			3.3.4.2 Company B: Assembly of a Smart Wallet Counter Display Model
	3.4 Pilots on the Shop Floor
		3.4.1 How Do We Set Up the Small-Scale Pilots?
		3.4.2 Use Case Descriptions: Results from Two Shop Floor Pilots with Operator Support Technology
			3.4.2.1 Company C: Precision Machining
				About
				The Pilot
				Results
			3.4.2.2 Company D: Sheltered Workspace
				About
				The Pilot
				Results
		3.4.3 Additional Examples: Two Short Test Descriptions
			3.4.3.1 Company E: Various Operator Support Solutions in Maintenance of Sorting Systems
			3.4.3.2 Company F: Manual Electronic Product Assembly Supported by Digital Work Instructions
	3.5 Business Case Analysis
		3.5.1 Quantifiable Costs and Benefits
		3.5.2 Non-quantifiable Costs and Benefits
		3.5.3 Use Case Descriptions: Was There a Business Case in Our Pilots?
			3.5.3.1 Company G, Sheltered Workplace2: Moderate to Strong Business Case
			3.5.3.2 Company H, Gearbox Assembly: Weak Business Case
			3.5.3.3 Company I, Step-by-Step Remote Assistance: Strong Business Case
	3.6 In Conclusion: Lessons Learnt and Future Developments?
		3.6.1 Implementing Cognitive Operator Support
		3.6.2 Our Methodology and Tools
			3.6.2.1 OS-Canvas
			3.6.2.2 Evaluating Usability
			3.6.2.3 Business Case
		3.6.3 Technology
		3.6.4 Future Developments
	References
4: Human-Centered Adaptive Assistance Systems for the Shop Floor
	4.1 Introduction
	4.2 Human-Centered Adaptivity
		4.2.1 Design Space for Adaptable Human-Centered Assistance
		4.2.2 Dimensions of Adaptive Assistance
			4.2.2.1 Goal of the Adaptation
	4.3 Three Exemplary Scenarios for Adaptivity
		4.3.1 Scenario 1
		4.3.2 Scenario 2
		4.3.3 Scenario 3
	4.4 Building Blocks for Adaptive Assistance
		4.4.1 Analysis of Existing Concepts and Implementations
		4.4.2 The Reference Architecture
		4.4.3 Knowledge Base
	4.5 Algorithms for Adaptive Behavior
		4.5.1 Rule-Based Approaches
		4.5.2 Methods of Machine Learning
		4.5.3 Suitable Adaptation Algorithms for the Scenarios
	4.6 Summary and Conclusion
	References
5: Deep Learning-Based Action Detection for Continuous Quality Control in Interactive Assistance Systems
	5.1 Introduction
	5.2 Related Work
	5.3 Concept
		5.3.1 Overall Architecture
		5.3.2 Assistance System
	5.4 Dataset
	5.5 Implementation
		5.5.1 Hardware
		5.5.2 Software
			5.5.2.1 Machine Learning System
			5.5.2.2 Model Generation
			5.5.2.3 Assistance System
	5.6 Evaluation
		5.6.1 Method
		5.6.2 Results
		5.6.3 Discussion
	5.7 Limitations
	5.8 Conclusion and Future Work
	References
6: Advancements in Vocational Training Through Mobile Assistance Systems
	6.1 Introduction
	6.2 Integration of Assistance Systems into Basic Training
		6.2.1 Embedding Complex Technical Systems
		6.2.2 Agility
		6.2.3 Inclusion of Different Levels of Education
		6.2.4 Place and Time-Independent Learning
		6.2.5 General Appeal of Vocational Training
	6.3 State of the Art
	6.4 Design and Implementation Concept
		6.4.1 The AS Modules
		6.4.2 Module 1: The Trainer Software
		6.4.3 Module 2: The Management Platform
		6.4.4 Module 3: Cloud Storage and Database
		6.4.5 Module 4: The Trainee Software
		6.4.6 Module 5: Training Insights
	6.5 Case Studies
		6.5.1 Study 1: Work 4.0
		6.5.2 Study 2: Joint Apprentice Workshop
	6.6 Conclusion and Outlook
	References
7: Designing User-Guidance for eXtendend Reality Interfaces in Industrial Environments
	7.1 Introduction: Why Do We Need Guidance Techniques in XR?
	7.2 Background
		7.2.1 Mixing Realities: What Are AR, VR, MR, XR?
		7.2.2 Specific Requirements of Industrial Applications
		7.2.3 Guidance in XR: Why Arrows Are Not Enough
	7.3 Related Work
		7.3.1 Guidance Applications in XR
		7.3.2 User Studies of Guidance Techniques
	7.4 Approach
		7.4.1 Design Processes and Process Integration
		7.4.2 Design: Activities and Support
		7.4.3 Support for Evaluation
	7.5 Reflection and Future Work
	References
8: Lenssembly: Authoring Assembly Instructions in Augmented Reality Using Programming-by-Demonstration
	8.1 Introduction
	8.2 Contribution Statement
	8.3 Related Work
		8.3.1 Augmented Reality Supported Assembly Guidance
		8.3.2 Assembly Authoring, Object, and Action Recognition
	8.4 Lenssembly: An Assembly Authoring and Playback System
		8.4.1 Authoring Mode: Expert Authoring and Recording Systems
		8.4.2 Playback Mode: Trainee Replay and Learning System
	8.5 Evaluation of Lenssembly Through a User Study
		8.5.1 Assembly Tasks
		8.5.2 Data Set Collection and Model Training
		8.5.3 Methodology
		8.5.4 Procedure
		8.5.5 Participants
	8.6 Results
		8.6.1 Task Completion Time
		8.6.2 Number of Errors and Task Load
		8.6.3 Qualitative Results
	8.7 Discussion
		8.7.1 Lenssembly Requires More Time than Paper Instructions
		8.7.2 Lenssembly Elicits Fewer Errors and Less Task Load
		8.7.3 Recording Assembly Instructions
		8.7.4 Limitations
		8.7.5 Future Work
	8.8 Conclusion
	References
9: Escaping the Holodeck: Designing Virtual Environments for Real Organizations
	9.1 Introduction
	9.2 Related Work
		9.2.1 Immersive Environments in Manufacturing
		9.2.2 Designing for Context of Use
		9.2.3 Designing Immersive Environments for Organizations
	9.3 Context and Research Methods
		9.3.1 Customizing the VR Environment
		9.3.2 Data Analysis and Procedures
	9.4 Findings
		9.4.1 Disrupting Workplace Norms
		9.4.2 Conflicting Realities
		9.4.3 Getting Lost in Translation
	9.5 Discussion
		9.5.1 Translating from 2D to 3D
		9.5.2 Translating from 3D Back to 2D
	9.6 Conclusion
	References
10: Gamification in Industrial Production: An Overview, Best Practices, and Design Recommendations
	10.1 Introduction
	10.2 The Background
		10.2.1 The Production Domain
		10.2.2 Gamification and Flow
		10.2.3 Recognizing Emotions to Sustain Flow
	10.3 Designing Gamification in Production from 2012 to 2021
		10.3.1 First Steps Towards Gamified Production
		10.3.2 Evaluating Design Variations and Branding
		10.3.3 Exploring Feedback Modalities
	10.4 Best Practices for Designing Gamification in Production
		10.4.1 Designing a Neat Integration into the Workplace
		10.4.2 Designing Branded Gamification for Specific Companies
		10.4.3 Designing Gamified Agents for Specific User Groups
	10.5 Design Recommendations
	References
11: New Industrial Work: Personalised Job Roles, Smooth Human-Machine Teamwork and Support for Well-Being at Work
	11.1 Introduction
	11.2 Related Work
		11.2.1 Industry 4.0 from Workers´ Point of View
		11.2.2 Well-Being at Work
		11.2.3 Operator 4.0 Visions
		11.2.4 Human-Centred Design of Industrial Systems
		11.2.5 Research Gap
	11.3 Surveys of Finnish Industry Workers
		11.3.1 Industrial Work in EU and in Finland
		11.3.2 Survey Methods
		11.3.3 Many Decades of Experience
		11.3.4 Investments in Human Capital
		11.3.5 Attitudes and Expectations Are Mainly Positive
		11.3.6 Work Safety and Well-Being
		11.3.7 A Range of Individuals
	11.4 A Vision of New Industrial Work: Personalised Job Roles, Smooth Human-Machine Teamwork and Support for Well-Being at Work
		11.4.1 Operator 4.0 Skills Dimensions and Personalised Job Roles
		11.4.2 Smooth Collaboration in Human-Machine Teams
		11.4.3 Well-Being at the Centre
	11.5 Recommendations for the Design of Factory Floor Solutions
	11.6 Conclusions
	References
12: Which Factors Influence Laboratory Employees´ Acceptance of Laboratory 4.0 Systems?
	12.1 Introduction
	12.2 Literature Review
		12.2.1 Laboratory 4.0
		12.2.2 Smart Home
		12.2.3 Commonalities and Differences Between Laboratory 4.0 and Smart Home
		12.2.4 Exploratory Factor Analysis
		12.2.5 Structural Equation Modeling
		12.2.6 Technology Acceptance Model
	12.3 Research Model and Hypothesis
		12.3.1 TAM Factors
		12.3.2 Personal Factors
	12.4 Methodology
	12.5 Results
		12.5.1 Descriptive Analysis
		12.5.2 Exploratory Factor Analysis
		12.5.3 Reflective Measurement Model
		12.5.4 Structural Model
		12.5.5 Supplemental Analysis
	12.6 Discussion
		12.6.1 Hypotheses
		12.6.2 Intention to Use
		12.6.3 Attitude Toward Use
		12.6.4 Usefulness and Ease of Use
		12.6.5 Laboratory 4.0 and Smart Home
		12.6.6 Trust and Perceived Risk
		12.6.7 Supplemental Analysis
		12.6.8 Limitations
	12.7 Conclusion
	Appendices
		Appendix 1: Questionnaire Items Used in the Survey
		Appendix 2: Laboratory 4.0 Model
		Appendix 3: Discriminant Validity: Fornell-Larcker Criterion
		Appendix 4: Discriminant Validity: Outer Loadings/Cross-Loadings
		Appendix 5: Collinearity Statistics (VIF)
		Appendix 6: Influence Paths and Hypotheses Results
		Appendix 7: Total Effects
	References
13: Determinants of Trust in Smart Technologies
	13.1 Introduction
	13.2 Theoretical Background
		13.2.1 Trust
		13.2.2 Trust in Smart Technologies
	13.3 Research Design
		13.3.1 Data Access and Sample
		13.3.2 Estimation Strategy
	13.4 Results
	13.5 Discussion
	13.6 Conclusion
	References
14: Interfaces, Interactions, and Industry 4.0: A Framework for the User-Centered Design of Industrial User Interfaces in the ...
	14.1 Introduction
	14.2 Industrial User Interfaces in an Internet of Production
		14.2.1 Challenges
		14.2.2 Context of the Research
	14.3 A Journey Through Different Industrial User Interfaces
		14.3.1 AR-Based Feed-Forward to Improve CFRP Product Quality
		14.3.2 Understanding Motives and Barriers to Human-Robot Collaboration
		14.3.3 What Happens When Autonomous Agents Face Moral Judgements?
		14.3.4 Understanding Information Processing When Handling Production Data
		14.3.5 Studying Basic Supply Chain Phenomena
		14.3.6 Supply Chains with Added Complexity: The Quality Management Game
	14.4 Destination and Conclusion of Our Journey
		14.4.1 The SIU Framework
		14.4.2 Application of the Framework
	14.5 Outlook and Future Journeys to Go
	References