Motion Capture and Ai in Dance Performance Analysis
Bibliographic Data
| ID | 22198684 |
|---|---|
| Authors | Nipun Setia (Chitkara University), Abhishek Kumar Singh (0000-0002-1327-8533), Lovish Dhingra (Chitkara University), Sonia Pandey (0000-0001-6661-5823), S K Singh Pandey (0000-0002-4079-2933, Noida International University), Mohit Aggarwal (0000-0001-8993-6064, Noida International University), Lakshman K (Jain University), Manisha Tushar Jadhav, Manisha Jadhav (International Institute of Information Technology) |
| Year | 2025 |
| Volume | 6 |
| Issue | 3s |
| Pages | 102-111 |
| Publication date | 2025-12-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v6.i3s.2025.6795 |
| OpenAlex | W7117249739 |
| Language | EN |
| References cited | 17 |
It is worth mentioning that the artificial intelligence (AI) combined with the application of motion capture technology has transformed the analysis of dance performances as it is studied and practiced. Motion capture technologies - both optical technologies with markers and optical technologies with inertial and visual technologies allow to measure human movement quantitatively and with high quality on a large scale, which is much more accurate and objective in comparison with classical methods of observation. The technologies involve the capture of kinematic, kinetic and spatial parameter that can be used in the evaluation of biomechanics, archive of choreography and high-resolution reconstruction of complicated sequences. At the same time, the AI techniques have become effective to explain this high dimensional movement data. Machine learning algorithms can be used to classify steps, styles and performance qualities in an automated way and deep learning models can identify more subtle patterns that differentiate artistically expressive or technical skill. The AI-based feedback systems can also offer corrective feedback, streamline the learning, and increase the effectiveness of the training. The intersection of Motion capture and AI has made possible new possibilities including multimodal data fusion, automatic scoring of skills, and performance tracking in real time. Such advances favour adaptive coaching mechanisms that are able to react dynamically to movement patterns of a dancer. Case studies of the recent prototypes of such research and commercial uses in professional dance, rehabilitation, and interactive media have been shown to be successful. Even though substantial gains have been registered, there are still difficulties concerning sensor less capture accuracy, diversity of datasets and personalization of AI models to diverse dance styles and bodies
Automatic identification and data capture · Dance · Inertial measurement unit · Match moving · Motion analysis · Motion Capture · Diversity and Impact of Dance · Flow Experience in Various Fields · Human Motion and Animation
| Highly cited | No |
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