What question did this study set out to answer?

The aim is to create a model that detects and corrects finger techniques in pipa players using video analysis.

March 15, 2026Open Access

Real-time detection and correction of pipa playing finger techniques based on video data analysis

Key Points

The aim is to create a model that detects and corrects finger techniques in pipa players using video analysis.
Utilized YOLO V11 for object detection of hands in video footage
Integrated HRNet for pose estimation based on extracted hand positioning data
Calculated deviations from standard finger positions to identify errors
Achieved 85% accuracy for dual-hand detection at an IoU threshold of 0.75
Attained 88% accuracy for hand pose estimation at a keypoint similarity threshold of 0.75

Abstract

This paper proposes a model integrating YOLO V11 with the high-resolution network (HRNet) for real-time detection and error correction of pipa fingering actions.The object detection method YOLO V11 is employed to rapidly and accurately locate the left and right hands playing the pipa in video footage.The extracted positioning data is then cropped and fed into the pose estimation algorithm HRNet.By calculating whether the output finger positions deviate from standard angles and coordinates beyond specified thresholds, the model identifies errors such as finger bending or wrist collapse.Through training, the proposed model achieves an average accuracy of 85% at an intersection over union (IoU) threshold of 0.75 for the dual-hand playing detection task.For the hand pose estimation task, it attains an average accuracy of 88% at a target keypoint similarity threshold of 0.75.

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