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Wearable Sensors for Human Health Monitoring and Analysis

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Dear Colleagues,

In recent times, many fields of research have been enhanced with the use of wearable sensors for the evaluation, monitoring and analysis of health in several contexts, including medical care, clinical assessment, home rehabilitation, industry, and others. Such sensors allow us to promote human-centred approaches to research, foster innovative applications and extend practical applications to improve people’s life.

This Special Issue “Wearable Sensors for Human Health Monitoring and Analysis” invites original contributions that explore the use of wearable sensors for health-related purposes, covering a wide range of topics, such as:

Novel projects and experiments that use wearable sensors to investigate health-related phenomena and outcomes;
Longitudinal assessments and evaluations of wearable sensor-based interventions and therapies in clinical settings;
Innovative sensor design, programming, and integration to enhance the performance, usability, and wearability of sensors;
Integration of wearable sensors for multi-domain approaches to health monitoring;
Novel algorithms and methods for processing, analyzing, and interpreting wearable sensors data;
Applications of wearable sensors for health promotion, prevention, diagnosis, treatment, and rehabilitation;
Ethical, social, and legal implications of using wearable sensors for health monitoring and analysis.

The Special Issue welcomes submissions from different areas and concerning different types of wearable sensors, such as inertial sensors, kinematic and motion sensors, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS) and functional NIRS, diffuse correlation spectroscopy (DCS), photopletismographic sensors, laser doppler flowrimetry (LDF), heart rate variability (HRV), electrodermal activity (EDA), smart clothes, bioelectrodes, and others. The Special Issue also encourages interdisciplinary and multidisciplinary approaches that combine wearable sensors with other technologies or modalities, such as virtual reality, augmented reality, artificial intelligence, machine learning, robotics, etc.

Dr. Alessandro Scano
Dr. Alfonso Mastropietro
Dr. Rebecca Re
Dr. Paolo Perego
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

wearable sensors
health
monitoring
analysis
EMG
EEG
fNIRS
artificial intelligence
physiology
rehabilitation

Published Papers (1 paper)

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Research

17 pages, 6954 KiB

Open AccessArticle

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO₂ and ΔHHB Measures for Comprehensive Analysis

by Muhammad Umar Khan, Maryam Sousani, Niraj Hirachan, Calvin Joseph, Maryam Ghahramani, Girija Chetty, Roland Goecke and Raul Fernandez-Rojas

Sensors 2024, 24(2), 458; https://doi.org/10.3390/s24020458 - 11 Jan 2024

Viewed by 921

Abstract

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain’s active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (

Δ H B O_{2}

) and deoxygenated haemoglobin (

Δ H H B

). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both

Δ H B O_{2}

and

Δ H H B

measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of

68.51 % \pm 9.02 %

in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of

Δ H B O_{2}

and

Δ H H B

. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)

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