HybriLoco
Gamification and Crowdsourcing for Collaborative 3D Construction
HybriLoco is an ongoing project aimed at developing a platform that leverages crowdsourcing and gamification to gather visual data from users in locomotion. This data is then sent to demoConstruct (a collaborative tool for reconstructing and editing digital twins), where it is processed into high-fidelity 3D digital twins. HybriLoco seeks to optimise how users can contribute to scene construction while retaining engagement through gamified interactions.
Currently, the project is in the system design and development stages. Key insights from a recently completed methodology review are used to refine the platform’s design and strategy. These insights include best practices in crowdsourcing mechanisms, user engagement techniques, and gamification elements to ensure effective data collection and sustained participation.
*Only a preview of the project is available due to ongoing work, despite it’s open-source nature.
Project Info
Work Project(open-source)
08 2023 - Present
Role
Lead of Research Package
Tasks
Conceptualisation
Product RoadmapLiteratur Review, Data Analysis
User Research & Experiments & Iterations
Topics
Gamification, Crowdsourcing
Human-Computer Interactions,
3D Construction, Collaborative Systems
Methods
System Design
Data-driven Research
Hard Skills
Figma, Unity, TBA..
With advancements in digital twins and 3D modeling, there has been a growing interest in integrating participatory methods for creating and maintaining large-scale, dynamic 3D environments. However, most traditional systems rely heavily on automated sensors and hardware, often neglecting the rich contextual data that human inputs can provide. Crowdsourcing offers a promising approach by enabling large-scale collaboration in data collection, while gamification ensures sustained user engagement. HybriLoco merges these principles, focusing on how users in motion within virtual environments can contribute to visual data collection, which is then processed to construct accurate and dynamic 3D models.
Research Question:
How can crowdsourcing and gamification strategies be effectively used to gather visual data for real-time 3D scene construction?
Sub Research Questions:
Research Question:
How can crowdsourcing and gamification strategies be effectively used to gather visual data for real-time 3D scene construction?
Sub Research Questions:
- What are the effective use of crowdsourcing mechanisms to gather and filter high-quality visual data for 3D scene construction?
- How can the integration of crowdsourcing and gamification improve the accuracy, quality, and efficiency of real-time 3D scene construction compared to traditional automated methods?
- What are the effective use of gamification strategies in maintaining high levels of user engagement and participation over time?
- What challenges arise from using crowdsourced data in 3D scene construction, and how can they be mitigated through design or system improvements?
Challenges & Opportunities.
5 key challenges are identified through a comprehensive review of existing approaches in crowdsourcing systems integrated with gamification for 3D scene construction. Respectively, these gaps reveal opportunities that highlight the need for HybriLoco.
1. Difficulty in Updating Digital Twins in Dynamic Environments
- key challenges:
Traditional methods struggle with real-time updates in rapid-changing environments where static models become outdated quickly. This impedes the wider application of digital twins.
- Opportunities:Crowdsourcing enables real-time data collection from users who interact with dynamic environments, contributing context-rich visual data (images, videos, spatial inputs). Gamification elements (e.g., rewards, feedback loops, progress tracking) incentivize continuous data submissions, ensuring dynamic synchronization of digital twins.
2. Lack of Flexibility in Data Integration
- key challenges:
Current methods are rigid and often fail to incorporate diverse and context-rich human contributions, reducing the overall richness and accuracy of the digital twin.
- Opportunities:
Crowdsourcing taps into diverse, user-generated data, offering contributions that hardware systems can’t capture (e.g., cultural context, on-the-ground conditions). Gamification provides structured tasks and rewards, ensuring focused, high-value contributions. This hybrid approach improves richness and adaptablity in data collection.
Crowdsourcing taps into diverse, user-generated data, offering contributions that hardware systems can’t capture (e.g., cultural context, on-the-ground conditions). Gamification provides structured tasks and rewards, ensuring focused, high-value contributions. This hybrid approach improves richness and adaptablity in data collection.
3. Motivational Decline in User Engagement Over Time
- key challenges:
Sustaining high user engagement in long-term crowdsourced task is challenging, impacting the quality and quantity of data collected
- Opportunities:
Gamification ensures sustained participation through long-term engagement strategies (e.g., leaderboards, achievements, rewards). This approach fosters ongoing user motivation, encouraging users to stay involved in data collection tasks. Continuous feedback loops also provide a sense of progress and recognition, combating engagement decline.
Gamification ensures sustained participation through long-term engagement strategies (e.g., leaderboards, achievements, rewards). This approach fosters ongoing user motivation, encouraging users to stay involved in data collection tasks. Continuous feedback loops also provide a sense of progress and recognition, combating engagement decline.
4. Inconsistencies in Data Quality
- key challenges:
Crowdsourced data can vary in quality due to diverse participant skills and engagement levels, which may result in inconsistent or low-quality inputs that can affect the accuracy of 3D construction.
- Opportunities:
Gamification introduces quality control mechanisms (e.g., peer reviews, ranking systems, quality badges), which reward contributors for high-accuracy data. Lower-quality contributors are guided toward improvement. Over time, this system ensures that only high-quality data is adopted.
Gamification introduces quality control mechanisms (e.g., peer reviews, ranking systems, quality badges), which reward contributors for high-accuracy data. Lower-quality contributors are guided toward improvement. Over time, this system ensures that only high-quality data is adopted.
5. Challenges in Synchronizing Crowdsourced Data with Hardware Sensors
- key challenges:
Aligning human-generated data with sensor data in real-time is a challenge due to asynchronous or varied data formats.
- Opportunities:
Gamification incentivizes users to submit data aligned with sensor data collection (e.g., through task timing or guided instructions)seamless synchronization for accurate, real-time 3D scene updates.
Gamification incentivizes users to submit data aligned with sensor data collection (e.g., through task timing or guided instructions)seamless synchronization for accurate, real-time 3D scene updates.
HybriLoco aims at addressing these challenges by building a crowdsourcing platform with gamification mechanisms to effectively enhance real-time updates, data integration, and user engagement in collaborative 3D construction.
Work-In-Progress.
I. Preview of Data Analysis
II. Actionable Insights for System Design (write-up)
1. Structured Task Guidance for Crowdsourcing Optimisation
A task-driven crowdsourcing system must ensure that users contribute data at critical points during motion within dynamic environments (e.g., walking through a virtual campus or city). Integrating visual cues or prompts that indicate where and when to collect data can optimize coverage and data quality.
2. Quality Control with Gamification
Introduce peer validation and real-time feedback to incentivize not just quantity, but also accuracy and relevancy. Allowing peers to validate contributions will help maintain a high standard of input while keeping users engaged.
3. Real-Time Data Synchronization Framework
Ensuring real-time synchronization between crowdsourced human input and hardware sensor data requires a system that prioritizes high-frequency, low-latency data transmission. The design of HybriLoco must incorporate a data validation layer, which checks for timestamp alignment and content quality, ensuring real-time coherence between user contributions and automated sensors.
4. Multi-layered and Iterative Incentivization for Long-Term Engagement
A multi-layered reward system can be used to balance short-term gratification with long-term recognition. Short-term engagement can be incentivized with instant rewards (e.g., points, tokens), while long-term contributions earn cumulative recognition, such as badges, access to exclusive features, or even real-world incentives. Embed continuous feedback into the platform, creating an iterative environment where users can witness how their contribution impact the 3D construction, and adjust in real-time, improving spatial accuracy.
5. Integrating Blockchain for Data Integrity
HybriLoco could explore the use of blockchain and decentralised data management to improve data integrity and will offer transparency, security, and trust, which are crucial when large-scale, high-stake applications.
6. Token-Based Incentives for Data Consistency
Build a token-based system that rewards consistency of user engagement over time. This aligns user contributions with project needs.
7. Expert Sourcing for High-Precision Tasks
For critical, accuracy-dependent contributions, such as fine-tuning specific parts of 3D models, expert sourcing should be incorporated. HybriLoco can assign structured tasks to experienced users or experts helps maintain relevancy and improves data accuracy.
III. Draft Product Development Plan
Phase 1: Platform Design and Development
Phase 1: Platform Design and Development
- Milestones:
> Develop a user-centered design by integrating real-time feedback loops and a token-based reward system, motivating both short- and long-term engagement.
> Design a task-driven crowdsourcing mechanism to prompt users at optimal points within virtual campuses, incorporating designed gamification strategies.
- Timeframe: X months
- Stakeholders: Anoymous
- Success Metrics: Functional feedback loop, token reward system live, and task-driven mechanism operational
- Risks: Complexity in user interaction leading to confusion
- Mitigation: Regular user testing and feedback integration during development
Phase 2: Prototype and Testing
- Milestones:
> Conduct beta testing with focused groups, deploying gamified mini-challenges to test engagement, and gather feedback to refine the crowdsourcing mechanism and gamification strategies.
> Run user experiments to evaluate engagement, data quality, and contribution rates, using evaluation criteria to validate the system's effectiveness.
- Timeframe: X months
- Stakeholders: Anoymous
- Success Metrics: High user engagement in beta, improved data quality through iterations, validated evaluation metrics
- Risks: Low user engagement or insufficient data quality in beta
- Mitigation: Iteratively refine beta challenges and rewards based on real-time feedback
Phase 3: Integration with demoConstruct
- Milestones:
> Develop a data processing pipeline that cleans, filters, and validates user-contributed data through peer reviews and gamified feedback.
> Ensure seamless integration between HybriLoco and demoConstruct by refining the data pipeline to align with 3D construction requirements.
- Timeframe: X months
- Stakeholders: Anoymous
- Success Metrics: Fully integrated data pipeline, validated data accuracy and alignment with demoConstruct
- Risks: Data misalignment or integration delays
- Mitigation: Early collaboration with demoConstruct and proactive testing for pipeline compatibility
Phase 4: Full-Scale Deployment
- Milestones:
> Scale the platform to a larger user base, expanding across virtual campuses and using iterative feedback to optimize engagement strategies and data synchronization processes.
> Implement progressive refinement based on real-time insights from user interactions, ensuring continuous improvements in system accuracy and efficiency.
- Timeframe: X months
- Stakeholders: Anoymous
- Success Metrics: User growth, sustained engagement, improved data accuracy and synchronization
- Risks: Lack of sustained user participation post-launch
- Mitigation: Ongoing iteration of engagement strategies based on real-time feedback
Phase 5: Community Building
- Milestones:
> Integrate social features to allow users to interact, share progress, and collaborate within the platform.
> Introduce community-based gamification (group challenges, leaderboards) to encourage collaboration and build a strong user base.
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Timeframe: X months
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Stakeholders: Anoymous
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Success Metrics: Increased community engagement, frequent user interactions, growth in collaborative tasks
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Risks: Low adoption of social features
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Mitigation: Gradual introduction of community features and incentives to encourage participation
*Project-in-progress