Paper year
2025
Paper dossier
Influence of rhythm features on beat/movement synchronization using a low-cost vision system
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Citations
1
Authors
0
Topic labels
0
Source readout
Source and corpus status
Venue
Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
Similarity rows
Not available yet
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8360, citation_velocity=0.0600, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1672)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0300)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0600, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0210)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Abstract
This study examines how musical expertise, tempo, and beat division influence synchronization accuracy and regularity in two movement tasks: finger tapping (discrete movements) and arm swing (continuous movements). Using a markerless motion capture system, we analyzed synchronization metrics across different rhythmic conditions. Motion data were extracted via AI-based pose estimation, and synchronization was computed by aligning movement peaks with beat times detected from audio stimuli. Results show that musicians exhibit higher synchronization accuracy and consistency than non-musicians, particularly in finger tapping tasks. Furthermore, simpler beat structures (binary rhythms) and moderate tempos facilitate better synchronization, whereas increased rhythmic complexity and tempo variability reduce performance. Interestingly, finger tapping leads to more precise synchronization than arm swing, suggesting that movement type significantly impacts rhythmic alignment. These findings support applications in therapy, training, and interactive systems, and demonstrate the value of AI-based motion tracking for scalable rhythm analysis.
Authors
No authors available.
Neighborhood labels
Topics
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Neighbor surface
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