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

2025-11-17

AI Summary

Addressing the challenge of jointly assessing biomechanical stability and psychological resilience in precision sports such as archery, this study proposes a lightweight, non-invasive multimodal intelligent analysis framework. A custom-designed wrist-worn device synchronously acquires acceleration and photoplethysmography (PPG) signals. We introduce SmoothDiff—a novel smoothed differential acceleration feature—that integrates motion dynamics with autonomic nervous system responses. An LSTM model identifies discrete movement phases, while heart rate variability (HRV) features coupled with an MLP classifier enable real-time stress estimation. Experimental results demonstrate 96.8% action recognition accuracy (F1-score = 95.9%) and 80% stress estimation accuracy. These findings robustly validate the efficacy and practicality of multimodal sensing for concurrent technical–psychological assessment in naturalistic training environments, establishing a new paradigm for intelligent, real-time feedback in precision sports.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Recognizing archery motion phases using wearable sensor data and LSTM models
• Estimating stress levels through PPG-based heart rate variability analysis
• Developing integrated motion-physiological sensing for athletic performance optimization

Innovation

• Machine learning framework integrates wearable sensor data
• LSTM model uses SmoothDiff feature for motion recognition
• MLP classifier analyzes HRV from PPG for stress estimation