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BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch

Taizhou Chen, Kai Chen, Xingyu Liu, Pingchuan Ke, Zhida Sun · Mar 23, 2026 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present adminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly describe the evaluation setup.
  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: General

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players.
  • We present adminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing.
  • Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • We present adminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing.
  • Our evaluations show that

Why It Matters For Eval

  • Our evaluations show that

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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