A Unified Approach for Real-Time Human Activity Recognition in Wearable Devices Using Attention-Gated Spatiotemporal Fusion Networks and Optimized Sensor Data Processing

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This preprint studies real-time human activity recognition in wearable devices, using multimodal time-series sensor data from 40 participants performing six sport-related activities, and evaluates a deep learning and sensor-fusion pipeline designed for dynamic transitions and efficient on-device computation. The authors propose an Attention-Gated Spatiotemporal Fusion Network that combines CNN and LSTM with an attention mechanism to prioritize relevant features, and an Adaptive Multimodal Kalman Fusion algorithm that adapts filtering to motion intensity and sensor variance; they further deploy the model using Dynamic Channel Pruning with Knowledge Distillation to reduce size and computation. They report state-of-the-art activity recognition accuracy (98.6%) along with real-time performance metrics including <90 ms latency, 0.85 bpm heart-rate monitoring error, and 35% energy reduction, while noting the work is a preprint not peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract The advent of wearable sensor technologies has transformed sports science and personal health monitoring, but real-time Human Activity Recognition (HAR) remains a challenge, especially in dynamic environments. Existing approaches often struggle to adapt to rapid activity transitions, handle multimodal sensor noise, and optimize for computational efficiency on resource-constrained wearable devices. In this work, we introduce an innovative system that overcomes these limitations through a novel integration of deep learning models and sensor fusion techniques. At its core, our framework features the Attention-Gated Spatiotemporal Fusion Network (AG-SFN), a deep neural network that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with a unique attention mechanism to dynamically prioritize the most relevant features from multimodal time-series data for accurate activity classification. To address sensor noise and improve data integrity, we propose the Adaptive Multimodal Kalman Fusion (AMKF) algorithm, which adapts the filtering process based on the motion intensity and sensor variance in real time. To ensure the efficient deployment of this model on wearable devices, we introduce a Dynamic Channel Pruning with Knowledge Distillation (DCP-KD) method, which significantly reduces the model size and computational load by pruning less relevant features and distilling knowledge from a large teacher model to a smaller student model. Our system was rigorously evaluated on a custom dataset of 40 participants performing six distinct sport-related activities, achieving a state-of-the-art activity recognition accuracy of 98.6%, outperforming conventional models by a significant margin. The system also demonstrated exceptional real-time performance with heart rate monitoring error reduced to 0.85 beats per minute (bpm), latency under 90 ms, and a 35% reduction in energy consumption. This research establishes a new benchmark for the development of efficient and adaptive wearable systems for real-time sports health monitoring, offering a robust solution for next-generation wearable technologies with practical applications in sports, fitness, and healthcare.
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A Unified Approach for Real-Time Human Activity Recognition in Wearable Devices Using Attention-Gated Spatiotemporal Fusion Networks and Optimized Sensor Data Processing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Unified Approach for Real-Time Human Activity Recognition in Wearable Devices Using Attention-Gated Spatiotemporal Fusion Networks and Optimized Sensor Data Processing Wei-Jian Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7401653/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The advent of wearable sensor technologies has transformed sports science and personal health monitoring, but real-time Human Activity Recognition (HAR) remains a challenge, especially in dynamic environments. Existing approaches often struggle to adapt to rapid activity transitions, handle multimodal sensor noise, and optimize for computational efficiency on resource-constrained wearable devices. In this work, we introduce an innovative system that overcomes these limitations through a novel integration of deep learning models and sensor fusion techniques. At its core, our framework features the Attention-Gated Spatiotemporal Fusion Network (AG-SFN), a deep neural network that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with a unique attention mechanism to dynamically prioritize the most relevant features from multimodal time-series data for accurate activity classification. To address sensor noise and improve data integrity, we propose the Adaptive Multimodal Kalman Fusion (AMKF) algorithm, which adapts the filtering process based on the motion intensity and sensor variance in real time. To ensure the efficient deployment of this model on wearable devices, we introduce a Dynamic Channel Pruning with Knowledge Distillation (DCP-KD) method, which significantly reduces the model size and computational load by pruning less relevant features and distilling knowledge from a large teacher model to a smaller student model. Our system was rigorously evaluated on a custom dataset of 40 participants performing six distinct sport-related activities, achieving a state-of-the-art activity recognition accuracy of 98.6%, outperforming conventional models by a significant margin. The system also demonstrated exceptional real-time performance with heart rate monitoring error reduced to 0.85 beats per minute (bpm), latency under 90 ms, and a 35% reduction in energy consumption. This research establishes a new benchmark for the development of efficient and adaptive wearable systems for real-time sports health monitoring, offering a robust solution for next-generation wearable technologies with practical applications in sports, fitness, and healthcare. Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Wearable Sensors Human Activity Recognition CNN-LSTM Model Compression Sports Health Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Existing approaches often struggle to adapt to rapid activity transitions, handle multimodal sensor noise, and optimize for computational efficiency on resource-constrained wearable devices. In this work, we introduce an innovative system that overcomes these limitations through a novel integration of deep learning models and sensor fusion techniques. At its core, our framework features the Attention-Gated Spatiotemporal Fusion Network (AG-SFN), a deep neural network that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with a unique attention mechanism to dynamically prioritize the most relevant features from multimodal time-series data for accurate activity classification. To address sensor noise and improve data integrity, we propose the Adaptive Multimodal Kalman Fusion (AMKF) algorithm, which adapts the filtering process based on the motion intensity and sensor variance in real time. To ensure the efficient deployment of this model on wearable devices, we introduce a Dynamic Channel Pruning with Knowledge Distillation (DCP-KD) method, which significantly reduces the model size and computational load by pruning less relevant features and distilling knowledge from a large teacher model to a smaller student model. Our system was rigorously evaluated on a custom dataset of 40 participants performing six distinct sport-related activities, achieving a state-of-the-art activity recognition accuracy of 98.6%, outperforming conventional models by a significant margin. The system also demonstrated exceptional real-time performance with heart rate monitoring error reduced to 0.85 beats per minute (bpm), latency under 90 ms, and a 35% reduction in energy consumption. 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