A Machine Learning-Based Multimodal Framework for Wearable Sensor-Based Archery Action Recognition and Stress Estimation

TL;DR

This paper presents a wearable sensor-based multimodal framework using machine learning to recognize archery actions and estimate stress levels, enabling real-time performance feedback in natural training environments.

Contribution

It introduces a novel feature and combines motion and physiological data with machine learning for simultaneous action recognition and stress estimation.

Findings

Achieved 96.8% accuracy in motion phase recognition.

Achieved 80% accuracy in stress level classification.

Demonstrated feasibility of real-time, non-intrusive monitoring in archery.

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.