# The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology

arXiv: https://arxiv.org/abs/2401.10877 · HTML: https://mlsanity.com/paper/2401.10877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 211 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [CheXclusion: Fairness gaps in deep chest X-ray classifiers](https://mlsanity.com/paper/2003.00827/) (2020)
- [The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons](https://mlsanity.com/paper/1912.04930/) (2019)
- [Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development](https://mlsanity.com/paper/2108.04308/) (2021)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020)
- [Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits](https://mlsanity.com/paper/2102.04256/) (2021)
- [A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability](https://mlsanity.com/paper/2106.12403/) (2021)
- [Data Vision: Learning to See Through Algorithmic Abstraction](https://mlsanity.com/paper/2002.03387/) (2020)
- [Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?](https://mlsanity.com/paper/2109.08131/) (2021)
- [German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation](https://mlsanity.com/paper/2206.09978/) (2022)
- [An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring](https://mlsanity.com/paper/2201.09151/) (2022)
- [Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation](https://mlsanity.com/paper/2011.00043/) (2020)
- [What Do We Mean When We Talk about Trust in Social Media? A Systematic Review](https://mlsanity.com/paper/2302.03671/) (2023)
- [NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks](https://mlsanity.com/paper/1910.03695/) (2019)

## Built on by (most cited first, 1 of 1)

- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)

## 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.