BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch
This provides accessible fine-grained stroke evaluation for amateur badminton players, but it is incremental as it applies existing sensing methods to a new domain.
The paper tackles the problem of evaluating badminton performance for amateur players by developing BadminSense, a smartwatch-based system that achieves 91.43% stroke classification accuracy, 0.438 average quality rating error, and 12.9% average impact location estimation error.
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.