The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology
arXiv:2401.10877 · doi:10.1145/3613904.3642004
Abstract
Motion capture systems, used across various domains, make body representations concrete through technical processes. We argue that the measurement of bodies and the validation of measurements for motion capture systems can be understood as social practices. By analyzing the findings of a systematic literature review (N=278) through the lens of social practice theory, we show how these practices, and their varying attention to errors, become ingrained in motion capture design and innovation over time. Moreover, we show how contemporary motion capture systems perpetuate assumptions about human bodies and their movements. We suggest that social practices of measurement and validation are ubiquitous in the development of data- and sensor-driven systems more broadly, and provide this work as a basis for investigating hidden design assumptions and their potential negative consequences in human-computer interaction.
34 pages, 9 figures. To appear in the 2024 ACM CHI Conference on Human Factors in Computing Systems (CHI '24)
References in corpus (7)
- The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
- Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
- Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
- Data Vision: Learning to See Through Algorithmic Abstraction
- Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
- What Do We Mean When We Talk about Trust in Social Media? A Systematic Review
- An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring