
Abstract: This paper examines how artificial intelligence mediates the detection and analysis of human movement in audiovisual contexts, particularly in film and advertising, treating movement as a complex object that cannot be reduced to technical measurability or expressive value. It shows how algorithmic translation affects the body, the meaning of the image, and the rights of the individuals involved, while optimization processes tend to normalize bodily gesture and automatic detection indirectly conditions aesthetic and narrative decisions. The study further situates bodily movement as information worthy of protection, raising questions about its capture, reuse, and circulation as data within automated systems. It argues that detecting movement is not equivalent to understanding it and concludes that preserving the plurality of interpretations of human movement requires treating artificial intelligence as a contextual tool, subordinated to narrative, functional, and normative frameworks. Note: Version updated in repository for preservation and digital accessibility. Original intellectual content remains unchanged. Official reference version:Zenodo DOI: https://doi.org/10.5281/zenodo.18463145Parallel version available:SSRN: https://ssrn.com/abstract=6168410 - DOI: https://doi.org/10.2139/ssrn.6168410Preservation copy at the Internet Archive: https://archive.org/details/zenodo.18463145
motion detection, human movement, artificial intelligence, bodily data, functional biomechanics
motion detection, human movement, artificial intelligence, bodily data, functional biomechanics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
