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

arXiv: https://arxiv.org/abs/2603.21825 · HTML: https://mlsanity.com/paper/2603.21825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 78 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Enabling hand gesture customization on wrist-worn devices](https://mlsanity.com/paper/2203.15239/) (2022)
- [PoseCoach: A Customizable Analysis and Visualization System for Video-based Running Coaching](https://mlsanity.com/paper/2204.08805/) (2022)
- [Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset](https://mlsanity.com/paper/2306.15664/) (2023)
- [iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations](https://mlsanity.com/paper/2303.03476/) (2023)
- [avaTTAR: Table Tennis Stroke Training with On-body and Detached Visualization in Augmented Reality](https://mlsanity.com/paper/2407.15373/) (2024)
- [VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching](https://mlsanity.com/paper/2307.12539/) (2023)
- [Wearable Audio and IMU Based Shot Detection in Racquet Sports](https://mlsanity.com/paper/1805.05456/) (2018)
- [BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports](https://mlsanity.com/paper/2502.21085/) (2025)
- [Silent Impact: Tracking Tennis Shots from the Passive Arm](https://mlsanity.com/paper/2507.23215/) (2025)

## Abstract (arXiv)

Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, 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 BadminSense achieves a stroke classification accuracy of 91.43%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9%. A real-world usability study further demonstrates BadminSense's potential to provide reliable and meaningful support for daily badminton practice.