A Machine Learning-Based Multimodal Framework for Wearable Sensor-Based Archery Action Recognition and Stress Estimation
Xianghe Liu, Jiajia Liu, Chuxian Xu, Minghan Wang, Hongbo Peng, Tao Sun, Jiaqi Xu
TL;DR
This paper tackles the need for integrated biomechanical and psychological state monitoring in precision sports, focusing on archery. It presents a wearable, multimodal framework that fuses accelerometer and PPG data to jointly recognize motion phases and estimate stress, using a novel SmoothDiff feature with LSTM for motion and an MLP for HRV-based stress classification. A synchronized dataset from six adolescent archers labeled with draw/aim/release and self-reported stress enables robust evaluation. The results show high motion-recognition accuracy up to 96.8% and stress-classification accuracy of 80%, demonstrating feasibility for real-world, real-time feedback and enabling data-driven training in archery and other precision disciplines.
Abstract
In precision sports such as archery, athletes' performance depends on both biomechanical stability and psychological resilience. Traditional motion analysis systems are often expensive and intrusive, limiting their use in natural training environments. To address this limitation, we propose a machine learning-based multimodal framework that integrates wearable sensor data for simultaneous action recognition and stress estimation. Using a self-developed wrist-worn device equipped with an accelerometer and photoplethysmography (PPG) sensor, we collected synchronized motion and physiological data during real archery sessions. For motion recognition, we introduce a novel feature--Smoothed Differential Acceleration (SmoothDiff)--and employ a Long Short-Term Memory (LSTM) model to identify motion phases, achieving 96.8% accuracy and 95.9% F1-score. For stress estimation, we extract heart rate variability (HRV) features from PPG signals and apply a Multi-Layer Perceptron (MLP) classifier, achieving 80% accuracy in distinguishing high- and low-stress levels. The proposed framework demonstrates that integrating motion and physiological sensing can provide meaningful insights into athletes' technical and mental states. This approach offers a foundation for developing intelligent, real-time feedback systems for training optimization in archery and other precision sports.