Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- 2 Interviews: People
- 3 Humanities and Human-Centered Machine Learning
- 4 Machine Learning in Movement-Based Interaction for Performing Arts Applications
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- Part IV Machine Learning in Its Social Context
- Part V Machine Learning and Humanity
- References
4 - Machine Learning in Movement-Based Interaction for Performing Arts Applications
from Part I - Human-Centered Machine Learning in the Arts and Humanities
Published online by Cambridge University Press: 11 September 2026
Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- 2 Interviews: People
- 3 Humanities and Human-Centered Machine Learning
- 4 Machine Learning in Movement-Based Interaction for Performing Arts Applications
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- Part IV Machine Learning in Its Social Context
- Part V Machine Learning and Humanity
- References
Summary
In this chapter we review fundamentals of machine learning for movement-based interaction in performing arts, describing typical interaction paradigms, workflows, and processes. Using examples, we describe how different tasks typically found in machine learning can be used or modified in this context. In particular, computational models can be used not only to recognize “which” gestures or human body movements are performed, but also “how” they are performed. Therefore, notions of expressivity, style, and movement qualities must be considered. Moreover, movement-based interactive systems face other specific challenges, from both machine learning and human–computer interaction perspectives that we review in this chapter: designing movements, limited datasets, and human learning.
Keywords
Information
- Type
- Chapter
- Information
- Human-Centered Machine Learning , pp. 77 - 104Publisher: Cambridge University PressPrint publication year: 2026
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