A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection

Zhang, Shibo; Nemati, Eghlim; Ahmed, Tousif; Rahman, Md. Mahbubur; Kuang, Jilong; Gao, Alex

doi:10.1109/embc46164.2021.9629993

Cited by 4 publications

(2 citation statements)

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“…Pulmonary disease, such as Chronic Obstructive Pulmonary Disease (COPD), asthma, and COVID-19, is one of leading causes of morbidity and mortality. Some recent works use wearables to detect cough activity, a major symptom of pulmonary diseases [37][38][39][40]. Other works have introduced methods for monitoring stroke in infants using wearable accelerometers [41] and methods for assessing depressive symptoms utilizing wrist-worn sensors [42].…”

Section: Wearables In Healthcare and Rehabilitationmentioning

confidence: 99%

“…For a single layer neural network these functions can be written as: Learning an autoencoder is an effective approach to perform dimensionality reduction and can be thought of as a strict generalization of PCA. Specifically, a 1-layer encoder with linear activation and mean squared error (MSE) loss (see Equation ( 3)) should be able to learn PCA transformation [38]. Nonetheless, deep models with several hidden layers and non-linear activation functions can learn better high-level and disentangled features from the original input data.…”

Section: Autoencodermentioning

confidence: 99%

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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

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250

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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