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Wearable Sensors for Human Health Monitoring and Analysis
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Wearable Sensors for Human Health Monitoring and Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 10 January 2025 | Viewed by 5411

Special Issue Editors

Dr. Alessandro Scano

E-Mail Website
Guest Editor
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milano, Italy
Interests: EMG; muscle synergies; motor control; neurological rehabilitation; signal analysis; kinematics; biomechanics; skeletal muscle; motion analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Alfonso Mastropietro

E-Mail Website
Guest Editor
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, Via Alfonso Corti 12, 20133 Milano, Italy
Interests: MRI; EEG; signal processing; medical image analysis; diffusion MRI; advanced MRI approaches; quantitative MRI; mental workload; central nervous system; stroke; skeletal muscle; rehabilitation
Special Issues, Collections and Topics in MDPI journals
Dr. Rebecca Re

E-Mail Website
Guest Editor
1. Politecnico di Milano, Dipartimento di Fisica, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2. Istituto di Fotonica e Nanotecnologie (IFN), Consiglio Nazionale delle Ricerche (CNR), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Interests: time-domain functional near-infrared spectroscopy; fNIRS device development; clinical translation of prototypes; data analysis and interpretation; biomedical application of fNIRS (cerebral activation, muscle oxidative metabolism, imaging, etc.); phantom development and tests for diffuse optics
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Perego

E-Mail Website
Guest Editor
Dipartimento di Design, Politecnico di Milano, Via Candiani 72, 20158 Milano, Italy
Interests: wearables; human–computer interaction; UX design rehabilitation; tele-medicine; mobile health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

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Published Papers (6 papers)

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Research

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12 pages, 1677 KiB  
Article
Validity and Test–Retest Reliability of Spatiotemporal Running Parameter Measurement Using Embedded Inertial Measurement Unit Insoles
by Louis Riglet, Baptiste Orliac, Corentin Delphin, Audrey Leonard, Nicolas Eby, Paul Ornetti, Davy Laroche and Mathieu Gueugnon
Sensors 2024, 24(16), 5435; https://doi.org/10.3390/s24165435 - 22 Aug 2024
Viewed by 442
Abstract
Running is the basis of many sports and has highly beneficial effects on health. To increase the understanding of running, DSPro® insoles were developed to collect running parameters during tasks. However, no validation has been carried out for running gait analysis. The [...] Read more.
Running is the basis of many sports and has highly beneficial effects on health. To increase the understanding of running, DSPro® insoles were developed to collect running parameters during tasks. However, no validation has been carried out for running gait analysis. The aims of this study were to assess the test–retest reliability and criterion validity of running gait parameters from DSPro® insoles compared to a motion-capture system. Equipped with DSPro® insoles, a running gait analysis was performed on 30 healthy participants during overground and treadmill running using a motion-capture system. Using an intraclass correlation coefficient (ICC), the criterion validity and test–retest reliability of spatiotemporal parameters were calculated. The test–retest reliability shows moderate to excellent ICC values (ICC > 0.50) except for propulsion time during overground running at a fast speed with the motion-capture system. The criterion validity highlights a validation of running parameters regardless of speeds (ICC > 0.70). This present study validates the good criterion validity and test–retest reliability of DSPro® insoles for measuring spatiotemporal running gait parameters. Without the constraints of a 3D motion-capture system, such insoles seem to be helpful and relevant for improving the care management of active patients or following running performance in sports contexts. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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18 pages, 4783 KiB  
Article
Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices
by Zahra Tohidinejad, Saeed Danyali, Majid Valizadeh, Ralf Seepold, Nima TaheriNejad and Mostafa Haghi
Sensors 2024, 24(16), 5219; https://doi.org/10.3390/s24165219 - 12 Aug 2024
Viewed by 819
Abstract
Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance [...] Read more.
Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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21 pages, 5617 KiB  
Article
Design Decisions for Wearable EEG to Detect Motor Imagery Movements
by Ana Carretero and Alvaro Araujo
Sensors 2024, 24(15), 4763; https://doi.org/10.3390/s24154763 - 23 Jul 2024
Viewed by 607
Abstract
The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine [...] Read more.
The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG’s ability to handle the algorithm parameters with good performance. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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17 pages, 6954 KiB  
Article
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 1614
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 [...] Read more.
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 (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). 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 ΔHBO2 and ΔHHB 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%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. 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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10 pages, 810 KiB  
Brief Report
The Utility of Calibrating Wearable Sensors before Quantifying Infant Leg Movements
by Jinseok Oh, Gerald E. Loeb and Beth A. Smith
Sensors 2024, 24(17), 5736; https://doi.org/10.3390/s24175736 - 4 Sep 2024
Viewed by 249
Abstract
While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration (g) is common among commercially available sensors. In this [...] Read more.
While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration (g) is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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14 pages, 922 KiB  
Perspective
Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies
by Alessandro Scano, Valentina Lanzani, Cristina Brambilla and Andrea d’Avella
Sensors 2024, 24(12), 3934; https://doi.org/10.3390/s24123934 - 18 Jun 2024
Cited by 1 | Viewed by 681
Abstract
Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing [...] Read more.
Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients’ pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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