Machine Learning and Deep Learning for Physiological Signal Analysis

Editorial

13 April 2022

Editorial: Machine Learning and Deep Learning for Physiological Signal Analysis

Rajesh Kumar Tripathy

Mario Arrieta Paternina

and

José Antonio de la O Serna

3,567 views

3 citations

Editors

RAJESH KUMAR TRIPATHY

Birla Institute of Technology and Science

Mario Arrieta Paternina

National Autonomous University of Mexico

José Antonio De La O Serna

Autonomous University of Nuevo León

Impact

(A) Hip flexion/extension (B) Ankle dorsi/plantarflexion (C) Knee flexion/extension. Average values of all the subjects data using the proposed system shown in blue color and the average values from all the subjects from the gait database shown in red color.

Original Research

05 January 2022

Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera

Aditya Viswakumar

, 3 more and

Chandu Parimi

8,439 views

43 citations

The boxplot distributions for each feature. The distribution value of Hjorth descriptor features for atrial fibrillation (AF), congestive heart failure (CHF), and normal sinus rhythm (NSR) conditions (A) and the distribution value of entropy-based features for AF, CHF, and NSR conditions (B).

Brief Research Report

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Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

Yunendah Nur Fuadah

and

Ki Moo Lim

3,254 views

15 citations

Original Research

07 January 2022

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Lei Jiang

, 2 more and

Noriaki Kuwahara

4,672 views

8 citations

Original Research

28 January 2022

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Yimin Wang

, 6 more and

Jinping Zheng

3,665 views

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Wearables devised for fatigue monitoring. ECG, electrocardiogram; EEG, electroencephalogram; EOG, electrooculogram; GSR, galvanic skin response; IMU, inertial motion unit; NIRS, near-infra-red spectroscopy; PPG, photoplethysmography; RES, respiration; TSk, skin temperature. 1Reprinted from Zhang et al. (2017). 2© (2015) IEEE. Reprinted, with permission, from Ko et al. (2015). 3Reprinted from Dhole et al., (2019). © (2019) with permission from Elsevier. 4© (2015) IEEE. Reprinted, with permission, from Li et al. (2015). 5Reprinted from Li and Chung (2015). 6Reprinted from Aryal et al. (2017). © (2017) with permission from Elsevier. 7Reprinted (adapted) with permission from Zeng et al. (2020). © (2020) American Chemical Society. 8Reprinted from Huang et al. (2018). © (2018), with permission from Elsevier. 9© (2018) IEEE. Reprinted, with permission, from Choi et al. (2018). 10© (2016) IEEE. Reprinted, with permission, from Ha and Yoo, (2016). 11© (2018) IEEE. Reprinted, with permission, from Nakamura et al. (2018). 12Republished with permission of SAE International, from Niwa et al. (2016); permission conveyed through Copyright Clearance Center, Inc. 13Reprinted with permission of Fuji Technology Press Ltd., from (Wang et al., 2018). 14© (2016) IEEE. Reprinted, with permission, from Mokaya et al. (2016).

Systematic Review

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Fatigue Monitoring Through Wearables: A State-of-the-Art Review

Neusa R. Adão Martins

, 2 more and

René M. Rossi

32,957 views

77 citations

Original Research

01 September 2021

Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

Patrick Thiam

, 3 more and

Friedhelm Schwenker

5,700 views

36 citations

Original Research

03 September 2021

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

Shakti Davis

, 17 more and

Albert Swiston

5,415 views

4 citations

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