Designing Efficient and Lightweight Deep Learning Models for Healthcare Analysis

Baltabay, Mereke; Yazıcı, Adnan; Sterling, Mark; Ever, Enver

doi:10.1007/s11063-023-11246-9

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Baltabay et al outline a framework for gathering and analyzing sensory data from various sensors, such as ECG and inertial sensors, which are then transformed into images using unique preprocessing methods. The proposed approach uses CNN with sensor fusion, random forest, and LSTM with GRU for evaluation and comparison with other models, including transfer learning with Mobile Net [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Wang,

Cui,

Fan

2023

PLoS ONE

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Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson’s health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons’ physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.

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Section: Related Workmentioning

confidence: 99%