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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 19342

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Special Issue Editors

Dr. Alberto Belli

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Guest Editor

Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: internet of things; wireless body sensor networks; data fusion algorithms; IMU sensors; application for biomedical devices
Special Issues, Collections and Topics in MDPI journals

Dr. Paola Pierleoni

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Guest Editor

Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: network protocols; wireless sensor network; internet of things; signal processing; embedded systems

Dr. Sara Raggiunto

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Guest Editor

Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: internet of things; wireless sensor networks; wireless body sensor networks; bluetooth mesh network; signal processing

Special Issue Information

Dear Colleagues,

Wearable sensors have drawn a lot of attention from research community during the last decade. They are increasingly used thanks to their unobtrusiveness, light weight, low cost, and ease of use for all-day and any-place. These technologies emerge in a wide range of applications for human motion analysis, such as Ambient Assisted Living, gait analysis, home-based rehabilitation, sport activities, etc.

The development of wearable sensor systems to allow continuous and real-time monitoring requires robust, secure, and energy saving data transmission.

The analysis of data generated from wearable sensors presents challenges in signal processing to provide reliable and relevant outputs. Therefore, innovative and intelligent solutions are needed to fully exploit this data.

In this context, this Special Issue of Applied Sciences on “Advances in Data Transmission and Analysis for Wearable Sensors” aims to connect researchers in the field of wearable sensors, focusing on data transmission and processing, in order to share ideas and conceptual approaches and to discuss the recent advances in this field, addressing innovative solutions and emerging issues. Topics of discussion include, but are not limited to, the exploration of new approaches in the areas of data transmission, data processing and data fusion in wearable sensors.

Dr. Alberto Belli
Dr. Paola Pierleoni
Dr. Sara Raggiunto
Guest Editors

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Keywords

wearable sensors
wireless body sensor networks
health monitoring systems
innovative applications of wearable sensor systems
energy efficient wearable systems
advanced sensor signal processing
data processing
data fusion

Published Papers (10 papers)

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Editorial

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2 pages, 160 KiB

Open AccessEditorial

Advances in Data Analysis for Wearable Sensors

by Alberto Belli, Paola Pierleoni and Sara Raggiunto

Appl. Sci. 2023, 13(9), 5487; https://doi.org/10.3390/app13095487 - 28 Apr 2023

Viewed by 658

Abstract

Wearable sensors have drawn a lot of attention from the research community during the last decade [...] Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

Research

Jump to: Editorial, Review

17 pages, 3006 KiB

Open AccessArticle

Noninvasive In Vivo Estimation of HbA1c Based on the Beer–Lambert Model from Photoplethysmogram Using Only Two Wavelengths

by Mrinmoy Sarker Turja, Tae-Ho Kwon, Hyoungkeun Kim and Ki-Doo Kim

Appl. Sci. 2023, 13(6), 3626; https://doi.org/10.3390/app13063626 - 12 Mar 2023

Cited by 3 | Viewed by 1446

Abstract

Glycated hemoglobin (HbA1c) is the most important factor in diabetes control. Since HbA1c reflects the average blood glucose level over the preceding three months, it is unaffected by a patient’s activity level or diet before a test. Noninvasive HbA1c measurement reduces both the pain and complications associated with fingertip piercing to collect blood. Photoplethysmography is helpful for measuring HbA1c without blood samples. Herein, only two wavelengths (615 and 525 nm) were used to estimate HbA1c noninvasively, where two different ratio calibrations were applied and their performances were compared to a work that used three wavelengths. For the fingertip type, the Pearson’s r values for HbA1c estimates were 0.896 and 0.905, considering the ratio calibrations for the blood vessel and whole finger models, respectively. Using another value (HbA1c) calibration in addition to the ratio calibrations, we could improve this performance such that the Pearson’s r values of the HbA1c levels were 0.929 and 0.930 for the blood vessel and whole finger models, respectively. In a previous study, using three wavelengths, the Pearson’s r values were 0.916 and 0.959 for the blood vessel and whole finger models, respectively. Here, the RCF of the

{S p O}_{2}

estimation was 0.986 when the

{S p O}_{2}

ratio calibration was applied, while in a previous study, the RCF values of the

{S p O}_{2}

estimation were 0.983 and 0.986 for the blood vessel and whole finger models, respectively. Thus, we have shown that HbA1c estimation using only two wavelengths has a comparable performance to previous studies. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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14 pages, 2287 KiB

Open AccessFeature PaperArticle

A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network

by Ahmed Wasif Reza, Muhammad Sazzad Hossain, Moonwar Al Wardiful, Maisha Farzana, Sabrina Ahmad, Farhana Alam, Rabindra Nath Nandi and Nazmul Siddique

Appl. Sci. 2023, 13(1), 312; https://doi.org/10.3390/app13010312 - 27 Dec 2022

Cited by 4 | Viewed by 1715

Abstract

Brain tumor is a severe health condition that kills many lives every year, and several of those casualties are from rural areas. However, the technology to diagnose brain tumors at an early stage is not as efficient as expected. Therefore, we sought to create a reliable system that can help medical professionals to identify brain tumors. Although several studies are being conducted on this issue, we attempted to establish a much more efficient and error-free classification method, which is trained with a comparatively substantial number of real datasets rather than augmented data. Using a modified VGG-16 (Visual Geometry Group) architecture on 10,153 MRI (Magnetic Resonance Imaging) images with 3 different classes (Glioma, Meningioma, and Pituitary), the network performs significantly well. It achieved a precision of 99.4% for Glioma, 96.7% for Meningioma, and 100% for Pituitary, with an overall accuracy of 99.5%. It also attained better results than several other existing CNN architectures and state-of-the-art work. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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13 pages, 3897 KiB

Open AccessArticle

A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study

by Paola Pierleoni, Sara Raggiunto, Alberto Belli, Michele Paniccia, Omid Bazgir and Lorenzo Palma

Appl. Sci. 2022, 12(11), 5486; https://doi.org/10.3390/app12115486 - 28 May 2022

Cited by 4 | Viewed by 1843

Abstract

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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8 pages, 521 KiB

Open AccessArticle

Estimation of Tidal Volume during Exercise Stress Test from Wearable-Device Measures of Heart Rate and Breathing Rate

by Agnese Sbrollini, Riccardo Catena, Francesco Carbonari, Alessio Bellini, Massimo Sacchetti, Laura Burattini and Micaela Morettini

Appl. Sci. 2022, 12(11), 5441; https://doi.org/10.3390/app12115441 - 27 May 2022

Cited by 4 | Viewed by 2450

Abstract

Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Under such conditions, wearable devices could provide valuable support to monitor vital signs, such as heart rate (HR) and breathing rate (BR). The aim of this study was to develop a model to estimate TV from wearable-device measures of HR and BR during exercise. HR and BR were acquired through the Zephyr Bioharness 3.0 wearable device in nine subjects performing incremental cycling tests. For each subject, TV during exercise was obtained with a metabolic cart (Cosmed). A stepwise regression algorithm was used to create the model using as possible predictors HR, BR, age, and body mass index; the model was then validated using a leave-one-subject-out cross-validation procedure. The performance of the model was evaluated using the explained variance (R²), obtaining values ranging from 0.65 to 0.72. The proposed model is a valid method for TV estimation with wearable devices and can be considered not subject-specific and not instrumentation-specific. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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15 pages, 4343 KiB

Open AccessArticle

Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait

by Hyunsoo Kim

Appl. Sci. 2022, 12(9), 4384; https://doi.org/10.3390/app12094384 - 26 Apr 2022

Cited by 4 | Viewed by 1490

Abstract

Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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20 pages, 3500 KiB

Open AccessArticle

Wearable Sensory Apparatus for Real-Time Feedback in Wearable Robotics

by Marko Munih, Zoran Ivanić and Roman Kamnik

Appl. Sci. 2021, 11(23), 11487; https://doi.org/10.3390/app112311487 - 03 Dec 2021

Cited by 2 | Viewed by 1709

Abstract

We describe the Wearable Sensory Apparatus (WSA) System, which has been implemented and verified in accordance with the relevant standards. It comprises the Inertial Measurement Units (IMUs), real-time wireless data transmission over Ultrawideband (UWB), a Master Unit and several IMU dongles forming the Wireless Body Area Network (WBAN). The WSA is designed for, but is not restricted to, wearable robots. The paper focuses on the topology of the communication network, the WSA hardware, and the organization of the WSA firmware. The experimental evaluation of the WSA incorporates the confirmation of the timing using the supply current WSA profile, measurements related to determining the less error prone position of the master device on the backpack, measurements of the quality of the data transfer in a real environment scenario, measurements in the presence of other microwave signals, and an example of raw IMU signals during human walking. Placement of the master device on the top of the backpack was found to be less error prone, with less than 0.02% packet loss for all the IMU devices placed on different body segments. The packet loss did not change significantly in public buildings or on the street. There was no impact of Wi-Fi bands on the WSA data transfer. The WSA hardware and firmware passed conformance testing in a certified lab. Most importantly, the WSA performed reliably in the laboratory and in clinical tests with exoskeletons and prostheses. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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20 pages, 1978 KiB

Open AccessArticle

Two-Stage Adaptive Relay Selection and Power Allocation Strategy for Cooperative CR-NOMA Networks in Underlay Spectrum Sharing

by Suoping Li, Wenwu Liang, Vicent Pla, Nana Yang and Sa Yang

Appl. Sci. 2021, 11(21), 10433; https://doi.org/10.3390/app112110433 - 06 Nov 2021

Cited by 8 | Viewed by 1488

Abstract

In this paper, we consider a novel cooperative underlay cognitive radio network based on non-orthogonal multiple access (CR-NOMA) with adaptive relay selection and power allocation. In secondary networks, dedicated relay assistance and user assistance are used to achieve communication between the base station and the far (and near) user. Here, a two-stage adaptive relay selection and power allocation strategy is proposed to maximize the achievable data rate of the far user while ensuring the service quality of near user. Furthermore, the closed-form expressions of outage probability of two secondary users are derived, respectively, under interference power constraints, revealing the impact of transmit power, number of relays, interference threshold and target data rate on system outage probability. Numerical results and simulations validate the advantages of the established cooperation and show that the proposed adaptive relay selection and power allocation strategy has better outage performance. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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14 pages, 4563 KiB

Open AccessArticle

Quantitative Analysis of Different Multi-Wavelength PPG Devices and Methods for Noninvasive In-Vivo Estimation of Glycated Hemoglobin

by Shifat Hossain, Chowdhury Azimul Haque and Ki-Doo Kim

Appl. Sci. 2021, 11(15), 6867; https://doi.org/10.3390/app11156867 - 26 Jul 2021

Cited by 9 | Viewed by 2722

Abstract

Diabetes is a serious disease affecting the insulin cycle in the human body. Thus, monitoring blood glucose levels and the diagnosis of diabetes in the early stages is very important. Noninvasive in vivo diabetes-diagnosis procedures are very new and require thorough studies to be error-resistant and user-friendly. In this study, we compare two noninvasive procedures (two-wavelength- and three-wavelength-based methods) to estimate glycated hemoglobin (HbA1c) levels in different scenarios and evaluate them with error level calculations. The three-wavelength method, which has more model parameters, results in a more accurate estimation of HbA1c even when the blood oxygenation (SpO₂) values change. The HbA1c-estimation error range of the two-wavelength model, due to change in SpO₂, is found to be from −1.306% to 0.047%. On the other hand, the HbA1c estimation error for the three-wavelength model is found to be in the magnitude of 10⁻¹⁴% and independent of SpO₂. The approximation of SpO₂ from the two-wavelength model produces a lower error for the molar concentration based technique (−4% to −1.9% at 70% to 100% of reference SpO₂) as compared to the molar absorption coefficient based technique. Additionally, the two-wavelength model is less susceptible to sensor noise levels (max SD of %error, 0.142%), as compared to the three-wavelength model (max SD of %error, 0.317%). Despite having a higher susceptibility to sensor noise, the three-wavelength model can estimate HbA1c values more accurately; this is because it takes the major components of blood into account and thus becomes a more realistic model. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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Review

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18 pages, 650 KiB

Open AccessReview

Effectiveness of Lower-Cost Strategies for Running Gait Retraining: A Systematic Review

by Lissandro M. Dorst, Vitor Cimonetti, Jefferson R. Cardoso, Felipe A. Moura and Rodrigo R. Bini

Appl. Sci. 2023, 13(3), 1376; https://doi.org/10.3390/app13031376 - 20 Jan 2023

Cited by 1 | Viewed by 2184

Abstract

The effectiveness of lower-cost equipment used for running gait retraining is still unclear. The objective of this systematic review was to evaluate the effectiveness of lower-cost equipment used in running gait retraining in altering biomechanical outcomes that may be associated with injuries. The literature search included all documents from MEDLINE, Web of Science, CINAHL, SPORTDiscus, and Scopus. The studies were assessed for risk of bias using an evaluation tool for cross-sectional studies. After screening 2167 initial articles, full-text screening was performed in 42 studies, and 22 were included in the systematic review. Strong evidence suggested that metronomes, smartwatches, and digital cameras are effective in running gait retraining programs as tools for intervention and/or evaluation of results when altering step cadence and foot strike patterns. Strong evidence was found on the effectiveness of accelerometers in interventions with feedback to reduce the peak positive acceleration (PPA) of the lower leg and/or footwear while running. Finally, we found a lack of studies that exclusively used lower-cost equipment to perform the intervention/assessment of running retraining. Full article

(This article belongs to the Special Issue Advances in Data Analysis for Wearable Sensors)

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