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Wearable and Mobile Sensors and Data Processing—2nd Edition

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2009

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Special Issue Editors

Dr. Alberto Greco

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Guest Editor

Research Center “E. Piaggio”, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy
Interests: biomedical signal processing; physiological modeling; wearable monitoring systems; wearable sensors; Bayesian modeling; machine learning; electrodermal activity; cardiovascular modeling; neuroscience; embedded systems; human-machine interaction

Dr. Alejandro Luis Callara

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Guest Editor

Department of Information Engineering, University of Pisa, 56126 Pisa, Italy
Interests: biomedical signal processing; physiological modeling; wearable systems; bioimaging; EEG; fMRI; neuroscience, brain connectivity; brain-computer interface; dynamic causal modeling; multivariate autoregressive modeling; granger causality

Special Issue Information

Dear Colleagues,

The market of wearable devices and systems is constantly growing and offers unlimited possibilities. Wearable and mobile systems encompass a range of heterogeneous devices such as smartwatches, wristbands, earwear, cameras, clothing, and lightweight sensors and can provide innovative services to target healthy populations and patients for managing and optimizing their wellbeing, health, and daily activities.

The rapid expansion of such devices has occurred in parallel with unrestrained advancements in the fields of big data analysis and cloud computing. Recently, new tools have been devised to manage large quantities of data, such as those recorded by wearable systems, and to perform real-time analyses extending single-user monitoring to the large-scale analysis of data acquired from millions of users.

In this context, there are still unlimited possibilities and many challenges surrounding wearable device design, connectivity, power consumption, safety, and comfort, as well as data handling and processing to extract hidden information or provide real-time automatic artifact recognition and correction, data visualization, and protection.

This Special Issue aims to bring together recent advances and cutting-edge research in the field of wearable and mobile sensor design and automatic signal processing for data acquisition and processing in lab settings and real ecological scenarios, as well as in 24-hour monitoring of physiological/pathological conditions.

Dr. Alberto Greco
Dr. Alejandro Luis Callara
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 and mobile systems for physiological signal monitoring
real-time signal processing of physiological signals in a lab setting and real-scenarios
wireless sensing
ultra-low-power consumption
flexible electronics devices
embedded systems for health monitoring
nanotechnologies and chemical sensors
miniaturized sensors for clinical applications
continuous non-invasive monitoring of chemical biomarkers (e.g., sweat, breath, tears)
integrated passive wireless transmission systems with applications to wearable devices
artifact reduction in wearable system recordings: hardware and/or software solutions
machine learning techniques and applications to wearable data
big data mining: large-scale analysis of wearable-systems-acquired data
personalized health management: modeling and analysis of single-subject heterogeneous data
emotion recognition from wearable and mobile systems data, e.g., speech recognition, social signal processing, facial expression analysis

Related Special Issue

Wearable and Mobile Sensors and Data Processing in Sensors (8 articles)

Published Papers (3 papers)

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Research

22 pages, 12760 KiB

Open AccessArticle

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings

by Michele Zanoletti, Pasquale Bufano, Francesco Bossi, Francesco Di Rienzo, Carlotta Marinai, Gianluca Rho, Carlo Vallati, Nicola Carbonaro, Alberto Greco, Marco Laurino and Alessandro Tognetti

Sensors 2024, 24(10), 3205; https://doi.org/10.3390/s24103205 - 17 May 2024

Viewed by 223

Abstract

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices’ performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing—2nd Edition)

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22 pages, 2052 KiB

Open AccessFeature PaperArticle

Detecting Leadership Opportunities in Group Discussions Using Off-the-Shelf VR Headsets

by Chenghao Gu, Jiadong Chen, Jiayi Zhang, Tianyuan Yang, Zhankun Liu and Shin’ichi Konomi

Sensors 2024, 24(8), 2534; https://doi.org/10.3390/s24082534 - 15 Apr 2024

Viewed by 617

Abstract

The absence of some forms of non-verbal communication in virtual reality (VR) can make VR-based group discussions difficult even when a leader is assigned to each group to facilitate discussions. In this paper, we discuss if the sensor data from off-the-shelf VR devices can be used to detect opportunities for facilitating engaging discussions and support leaders in VR-based group discussions. To this end, we focus on the detection of suppressed speaking intention in VR-based group discussions by using personalized and general models. Our extensive analysis of experimental data reveals some factors that should be considered to enable effective feedback to leaders. In particular, our results show the benefits of combining the sensor data from leaders and low-engagement participants, and the usefulness of specific HMD sensor features. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing—2nd Edition)

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20 pages, 3040 KiB

Open AccessArticle

Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors

by Yedukondala Rao Veeranki, Sergi Garcia-Retortillo, Zacharias Papadakis, Andreas Stamatis, Kwadwo Osei Appiah-Kubi, Emily Locke, Ryan McCarthy, Ahmed Ali Torad, Ahmed Mahmoud Kadry, Mostafa Ali Elwan, Ali Boolani and Hugo F. Posada-Quintero

Sensors 2024, 24(5), 1425; https://doi.org/10.3390/s24051425 - 22 Feb 2024

Viewed by 776

Abstract

This study investigated the impact of auditory stimuli on muscular activation patterns using wearable surface electromyography (EMG) sensors. Employing four key muscles (Sternocleidomastoid Muscle (SCM), Cervical Erector Muscle (CEM), Quadricep Muscles (QMs), and Tibialis Muscle (TM)) and time domain features, we differentiated the effects of four interventions: silence, music, positive reinforcement, and negative reinforcement. The results demonstrated distinct muscle responses to the interventions, with the SCM and CEM being the most sensitive to changes and the TM being the most active and stimulus dependent. Post hoc analyses revealed significant intervention-specific activations in the CEM and TM for specific time points and intervention pairs, suggesting dynamic modulation and time-dependent integration. Multi-feature analysis identified both statistical and Hjorth features as potent discriminators, reflecting diverse adaptations in muscle recruitment, activation intensity, control, and signal dynamics. These features hold promise as potential biomarkers for monitoring muscle function in various clinical and research applications. Finally, muscle-specific Random Forest classification achieved the highest accuracy and Area Under the ROC Curve for the TM, indicating its potential for differentiating interventions with high precision. This study paves the way for personalized neuroadaptive interventions in rehabilitation, sports science, ergonomics, and healthcare by exploiting the diverse and dynamic landscape of muscle responses to auditory stimuli. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing—2nd Edition)

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