The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 211 references that are in the map. First posted on arXiv 19 Jan 2024.
Builds on
18 of 211 references.
References · 18
- 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
- 2019Measurement and Fairnesscited by 299
- 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
- 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
- 2021Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Developmentcited by 85
- 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
- 2022A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithmscited by 30
- 2020Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projectscited by 24
- 2021Problematic Machine Behavior: A Systematic Literature Review of Algorithm Auditscited by 20
- 2021A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditabilitycited by 16
- 2020Data Vision: Learning to See Through Algorithmic Abstractioncited by 11
6 more
- 2021Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?cited by 11
- 2022German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovationcited by 11
- 2022An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiringcited by 10
- 2020Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretationcited by 6
- 2023What Do We Mean When We Talk about Trust in Social Media? A Systematic Reviewcited by 4
- 2019NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networkscited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.