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

TL;DR

BadminSense is a smartwatch-based system that enables detailed badminton stroke analysis, including classification, quality assessment, and impact location estimation, using wearable sensors and a specialized dataset.

Contribution

It introduces a novel smartwatch system with a new annotated dataset for fine-grained badminton performance analysis, enabling real-time stroke evaluation.

Findings

Stroke classification accuracy of 91.43%

Average stroke quality rating error of 0.438

Impact location estimation error of 12.9%

Abstract

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.