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Analyzing dance moves and routines is a foundational step in learning dance. Videos are often utilized at this step, and advancements in machine learning, particularly in human-movement recognition, could further assist dance learners. We developed and evaluated a Wizard-of-Oz prototype of a video comprehension tool that offers automatic in-situ dance move identification functionality. Our system design was informed by an interview study involving 12 dancers to understand the challenges they face when trying to comprehend complex dance videos and taking notes. Subsequently, we conducted a within-subject study with 8 Cuban salsa dancers to identify the benefits of our system compared to an existing traditional feature-based search system. We found that the quality of notes taken by participants improved when using our tool, and they reported a lower workload. Based on participants’ interactions with our system, we offer recommendations on how an AI-powered span-search feature can enhance dance video comprehension tools.
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DanceBits 'It tells you to see us': Supporting Dance Practices with an Educational Computing Kit
Wearable electronics expand the ways learners can create with computing as they gain proficiency with programming and electronics. Dance is one domain where wearables can support creative, embodied practices in computing education. However, wearable electronics need to be small, durable, and easily integrated into clothing to meet the constraints of dance contexts. These features are challenging to achieve, especially when working with novices. We present DanceBits, a wearable prototyping kit for dance that was co-developed with a justice-oriented, computing and dance education organization. DanceBits’ plug-and-play system uses small PCBs with solderless connectors to support dancers in rapidly designing, building, and performing with electronic costumes. Our user studies exploring the system with dance instructors and youth participants show that DanceBits enabled fast development of wearables, offered users a breadth of expressivity through computational and choreographic choices, and empowered dancers to see wearables as a tool for developing their movement practices.
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- Award ID(s):
- 2241809
- PAR ID:
- 10503912
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- International Conference on Tangible, Embedded, and Embodied Interaction
- ISBN:
- 9798400704024
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Location:
- Cork Ireland
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3623509.3633350
