Floor sensors are cheap and easy to use! A Nihon Buyo Case Study
August 17, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Miho Imai
arXiv ID
2508.19261
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
As floor-sensing technologies gain traction in movement research, questions remain about their usability and effectiveness for non-expert users. This study presents a case study evaluating Flexel, a modular, low-cost, high-resolution pressure-sensing floor interface, in the context of Nihon Buyo, a traditional Japanese dance. The system was installed, calibrated, and used by a first-time, non-technical user to track weight distribution patterns of a teacher and learner over nine weeks. Live pressure data was synchronized with video recordings, and custom software was developed to process and analyze the signal. Despite expectations that the learner's weight distribution would converge toward the teacher's over time, quantitative analyses revealed that the learner developed a consistent yet distinct movement profile. These findings suggest that even within rigid pedagogical structures, individual movement signatures can emerge. More importantly, the study demonstrates that Flexel can be deployed and operated effectively by non-expert users, highlighting its potential for broader adoption in education, performance, and embodied research.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted