Sep 2026· International Journal of e-collaboration· 0 citations· 16 references
Human Pose and Action Recognition
TL;DR
MMAF-Net is proposed, a multi-branch deep learning architecture that integrates visual, pose-estimation and inertial sensor streams to extract complementary motion features, fused via a temporal attention module to classify 23 karate action types accurately.
Abstract
Karate training relies on subjective manual evaluation of intricate motions, hindering scalable, consistent coaching. This paper proposes MMAF-Net, a multimodal AI framework for real-time karate action recognition and automated performance feedback. Its three-branch deep learning architecture integrates visual, pose-estimation and inertial sensor streams to extract complementary motion features, fused via a temporal attention module to classify 23 karate action types accurately. Trained and validated on MS-KARD (2.8 million video frames and 5.6 million sensor readings from dual cameras and three IMUs), the model generates explainable coaching tips through rule-based modules referencing prediction confidence, posture bias and motion stability. Tests yield 96.3% accuracy and 95.1% F1-score, outperforming benchmarks like KarateNet. With only 24 ms inference latency, this real-time system suits interactive martial arts training scenarios.
A joint framework combining YOLOv8 and time-optimized OpenPose to mitigate pose estimation jitter and detection inaccuracies caused by rapid motion and occlusion in human motion analysis and offers technical reference for multimodal perception and dynamic scene understanding in advanced electromagnetic sensing applicat...
D. Zhao, Y.-Q. Ma· Advanced Electromagnetics· 0 citations
Pose-ARPA is proposed, a unified framework that combines deep pose estimation with spatiotemporal representation learning for comprehensive action recognition and performance analysis and achieves superior recognition accuracy, robust generalization under noisy or occluded conditions, and real-time inference capability...
Qi Yang, Long-Hui Wen· International Conference on...· 0 citations
Developing microanastomosis skill requires repeated practice with timely, action-specific feedback, yet expert review of lengthy microscope videos does not scale to frequent or distributed training. We present an integrated video-AI platform that turns a complete simulated procedure into inspectable, interactive feedba...
By combining vision-based sensing with temporal sequence learning, the proposed method provides an effective solution for objective motion evaluation and intelligent feedback generation, while offering valuable methodological references for real-time signal interpretation, human-centered electromagnetic sensing, and ne...
X. Zhou, W. He· Advanced Electromagnetics· 0 citations
Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision. This paper presents a telerehabilitation pipeline that integrates skeleton-based exercise quality assessment and short-term motion prediction into a two-...
Lara Pereira, J. Paulo, P. Santos et al.· 0 citations
This study provides a replicable technical path for the validation of rehabilitation evaluation algorithms without clinical data collection through the adaptive fusion mechanism to dynamically integrate the confidence of the deep network and the matching score of dynamic time warping template.
Mingxiang Yang· Journal of Discovery Core· 1 citation
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