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Body Sensor Networks and Wearables for Health Monitoring

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 21333

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Dr. Dario Salvi

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Department of Computer Science and Media Technology, Malmo University, 211 19 Malmo, Sweden
Interests: eHealth; mobile-health; digital-health; telehealth; IoT; IoMT; medical devices; medical informatics; AI; signal processing; sensor networks; wearable devices; embedded systems; user aspects; usability and acceptance; privacy; trust
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Special Issue Information

Dear Colleagues,

Nowadays, wearable sensors are used in a number of applications, from fashion to fitness and security; however, the most promising application of such technology is within healthcare. When combined with connectivity (thus becoming "Internet of Things" devices), wearable sensors can be exploited for the real-time monitoring of physiology (e.g., heart rate, respiration, sleep) and activities (e.g., steps, movement, activity detection). These data can be used to support healthcare, improve or automatize diagnosis and treatments, and foster healthy and independent living. Along with benefits, important challenges come in terms of interoperability, reliability, power consumption, usability, ergonomicity, data quality, data protection, security, and reliability. This special issue aims to explore these challenges and their potential solutions.

The main topics of this Special Issue include, but are not limited to, the following:

Low power wireless communication for wearable sensors;
Body-centric wireless networks;
Interoperability and standards for wearable health monitoring;
Security models and technologies for wearable health monitoring;
Applications of networked wearable sensors for health monitoring;
Algorithms for extracting meaningful information from wearable sensors;
Data collection and processing architectures;
Usability, ergonomicity, and biocompatibility of wearable devices and their applications;
Users' perceptions about the privacy and reliability of wearable health monitoring;
Clinical trials and pilot studies involving body sensor networks and wearable health monitoring.

Dr. Dario Salvi
Guest Editor

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Keywords

body sensor networks
wearable sensors
health monitoring
Internet of Medical Things
mobile-health
digital-health

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Published Papers (11 papers)

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Research

23 pages, 40206 KiB

Open AccessArticle

Development of a Body-Worn Textile-Based Strain Sensor: Application to Diabetic Foot Assessment

by Rory P. Turnbull, Jenny Corser, Giorgio Orlando, Prabhuraj D. Venkatraman, Irantzu Yoldi, Kathrine Bradbury, Neil D. Reeves and Peter Culmer

Sensors 2025, 25(7), 2057; https://doi.org/10.3390/s25072057 - 26 Mar 2025

Viewed by 378

Abstract

Diabetic Foot Ulcers (DFUs) are a significant health and economic burden, potentially leading to limb amputation, with a severe impact on a person’s quality of life. During active movements like gait, the monitoring of shear has been suggested as an important factor for effective prevention of DFUs. It is proposed that, in textiles, strain can be measured as a proxy for shear stress at the skin. This paper presents the conceptualisation and development of a novel strain-sensing approach that can be unobtrusively integrated within sock textiles and worn within the shoe. Working with close clinical and patient engagement, a sensor specification was identified, and 12 load-sensing approaches for the prevention of DFU were evaluated. A lead concept using a conductive adhesive was selected for further development. The method was developed using a Lycra sample, before being translated onto a knitted ‘sock’ substrate. The resultant strain sensor can be integrated within mass-produced textiles fabricated using industrial knitting machines. A case-study was used to demonstrate a proof-of-concept version of the strain sensor, which changes resistance with applied mechanical strain. A range of static and dynamic laboratory testing was used to assess the sensor’s performance, which demonstrated a resolution of 0.013

Ω

across a range of 0–430

Ω

and a range of interest of 0–20

Ω

. In cyclic testing, the sensor exhibited a cyclic strain threshold of 6% and a sensitivity gradient of 0.3 ± 0.02, with a low dynamic drift of 0.039 to 0.045% of the total range. Overall, this work demonstrates a viable textile-based strain sensor capable of integration within worn knitted structures. It provides a promising first step towards developing a sock-based strain sensor for the prevention of DFU formation. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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22 pages, 4342 KiB

Open AccessArticle

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios

by Alessandro Orro, Gian Angelo Geminiani, Francesco Sicurello, Marcello Modica, Francesco Pegreffi, Luca Neri, Antonio Augello and Matteo Botteghi

Sensors 2024, 24(21), 6992; https://doi.org/10.3390/s24216992 - 30 Oct 2024

Viewed by 1690

Abstract

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system’s reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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18 pages, 2137 KiB

Open AccessArticle

Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals

by Gabriel Ng, Aliaa Gouda and Jan Andrysek

Sensors 2024, 24(19), 6431; https://doi.org/10.3390/s24196431 - 4 Oct 2024

Viewed by 1229

Abstract

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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15 pages, 815 KiB

Open AccessArticle

Differences in Trunk Acceleration-Derived Gait Indexes in Stroke Subjects with and without Stroke-Induced Immunosuppression

by Luca Martinis, Stefano Filippo Castiglia, Gloria Vaghi, Andrea Morotti, Valentina Grillo, Michele Corrado, Federico Bighiani, Francescantonio Cammarota, Alessandro Antoniazzi, Luca Correale, Giulia Liberali, Elisa Maria Piella, Dante Trabassi, Mariano Serrao, Cristina Tassorelli and Roberto De Icco

Sensors 2024, 24(18), 6012; https://doi.org/10.3390/s24186012 - 17 Sep 2024

Viewed by 1275

Abstract

Background: Stroke-induced immunosuppression (SII) represents a negative rehabilitative prognostic factor associated with poor motor performance at discharge from a neurorehabilitation unit (NRB). This study aims to evaluate the association between SII and gait impairment at NRB admission. Methods: Forty-six stroke patients (65.4 ± 15.8 years, 28 males) and 42 healthy subjects (HS), matched for age, sex, and gait speed, underwent gait analysis using an inertial measurement unit at the lumbar level. Stroke patients were divided into two groups: (i) the SII group was defined using a neutrophil-to-lymphocyte ratio ≥ 5, and (ii) the immunocompetent (IC) group. Harmonic ratio (HR) and short-term largest Lyapunov’s exponent (sLLE) were calculated as measures of gait symmetry and stability, respectively. Results: Out of 46 patients, 14 (30.4%) had SII. HR was higher in HS when compared to SII and IC groups (p < 0.01). HR values were lower in SII when compared to IC subjects (p < 0.01). sLLE was lower in HS when compared to SII and IC groups in the vertical and medio-lateral planes (p ≤ 0.01 for all comparisons). sLLE in the medio-lateral plane was higher in SII when compared to IC subjects (p = 0.04). Conclusions: SII individuals are characterized by a pronounced asymmetric gait and a more impaired dynamic gait stability. Our findings underline the importance of devising tailored rehabilitation programs in patients with SII. Further studies are needed to assess the long-term outcomes and the role of other clinical features on gait pattern. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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13 pages, 1045 KiB

Open AccessArticle

Relationship between Fear-Avoidance Beliefs and Muscle Co-Contraction in People with Knee Osteoarthritis

by Takanori Taniguchi, So Tanaka, Tomohiko Nishigami, Ryota Imai, Akira Mibu and Takaaki Yoshimoto

Sensors 2024, 24(16), 5137; https://doi.org/10.3390/s24165137 - 8 Aug 2024

Viewed by 3072

Abstract

Excessive muscle co-contraction is one of the factors related to the progression of knee osteoarthritis (OA). A previous study demonstrated that pain, joint instability, lateral thrust, weight, and lower extremity alignment were listed as factors affecting excessive co-contraction in knee OA. However, this study aimed to assess the association between fear-avoidance beliefs and muscle co-contraction during gait and stair climbing in people with knee OA. Twenty-four participants with knee OA participated in this cross-sectional study. Co-contraction ratios (CCRs) were used to calculate muscle co-contraction during walking and stair climbing, using surface electromyography. Fear-avoidance beliefs were assessed by the Tampa Scale for Kinesiophobia-11 (TSK-11) for kinesiophobia and the Pain Catastrophizing Scale (PCS) for pain catastrophizing. Secondary parameters that may influence co-contraction, such as degree of pain, lateral thrust, weight, and lower extremity alignment, were measured. The relationships between the CCR during each movement, TSK-11, and PSC were evaluated using Spearman’s rank correlation coefficient and partial correlation analysis, adjusted by weight and lower extremity alignment. Partial correlation analysis showed a significant correlation only between medial muscles CCR and TSK-11 during stair descent (r = 0.54, p < 0.05). Our study revealed that kinesiophobia could be associated with co-contraction during stair descent in people with knee OA. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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12 pages, 1322 KiB

Open AccessArticle

Center of Pressure Measurement Accuracy via Insoles with a Reduced Pressure Sensor Number during Gaits

by Philip X. Fuchs, Wei-Han Chen and Tzyy-Yuang Shiang

Sensors 2024, 24(15), 4918; https://doi.org/10.3390/s24154918 - 29 Jul 2024

Cited by 1 | Viewed by 1512

Abstract

The objective was to compare simplified pressure insoles integrating different sensor numbers and to identify a promising range of sensor numbers for accurate center of pressure (CoP) measurement. Twelve participants wore a 99-sensor Pedar-X insole (100 Hz) during walking, jogging, and running. Eight simplified layouts were simulated, integrating 3–17 sensors. Concordance correlation coefficients (CCC) and root mean square errors (RMSE) between the original and simplified layouts were calculated for time-series mediolateral (ML) and anteroposterior (AP) CoP. Differences between layouts and between gait types were assessed via ANOVA and Friedman test. Concordance between the original and simplified layouts varied across layouts and gaits (CCC: 0.43–0.98;

χ_{(7)}^{2}

≥ 34.94, p < 0.001). RMSE_ML and RMSE_AP [mm], respectively, were smaller in jogging (5 ± 2, 15 ± 9) than in walking (8 ± 2, 22 ± 4) and running (7 ± 4, 20 ± 7) (

η_{p}^{2}

: 0.70–0.83, p < 0.05). Only layouts with 11+ sensors achieved CCC ≥ 0.80 in all tests across gaits. The 13-sensor layout achieved CCC ≥ 0.95 with 95% confidence, representing the most promising compromise between sensor number and CoP accuracy. Future research may refine sensor placement, suggesting the use of 11–13 sensors. For coaches, therapists, and applied sports scientists, caution is recommended when using insoles with nine or fewer sensors. Consulting task-specific validation results for the intended products is advisable. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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16 pages, 2183 KiB

Open AccessArticle

Quality-Aware Signal Processing Mechanism of PPG Signal for Long-Term Heart Rate Monitoring

by Win-Ken Beh, Yu-Chia Yang and An-Yeu Wu

Sensors 2024, 24(12), 3901; https://doi.org/10.3390/s24123901 - 16 Jun 2024

Cited by 1 | Viewed by 2186

Abstract

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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19 pages, 6469 KiB

Open AccessArticle

The Influence of Fatigue, Recovery, and Environmental Factors on the Body Stability of Construction Workers

by Daehwi Jo and Hyunsoo Kim

Sensors 2024, 24(11), 3469; https://doi.org/10.3390/s24113469 - 28 May 2024

Cited by 2 | Viewed by 2137

Abstract

In the construction industry, falls, slips, and trips (FST) account for 42.3% of all accidents. The primary cause of FST incidents is directly related to the deterioration of workers’ body stability. To prevent FST-related accidents, it is crucial to understand the interaction between physical fatigue and body stability in construction workers. Therefore, this study investigates the impact of fatigue on body stability in various construction site environments using Dynamic Time Warping (DTW) analysis. We conducted experiments reflecting six different fatigue levels and four environmental conditions. The analysis process involves comparing changes in DTW values derived from acceleration data obtained through wearable sensors across varying fatigue levels and construction environments. The results reveal the following changes in DTW values across different environments and fatigue levels: for non-obstacle, obstacle, water, and oil conditions, DTW values tend to increase as fatigue levels rise. In our experiments, we observed a significant decrease in body stability against external environments starting from fatigue Levels 3 or 4 (30% and 40% of the maximum failure point). In the non-obstacle condition, the DTW values were 9.4 at Level 0, 12.8 at Level 3, and 23.1 at Level 5. In contrast, for the oil condition, which exhibited the highest DTW values, the values were 10.5 at Level 0, 19.1 at Level 3, and 34.5 at Level 5. These experimental results confirm that the body stability of construction workers is influenced by both fatigue levels and external environmental conditions. Further analysis of recovery time, defined as the time it takes for body stability to return to its original level, revealed an increasing trend in recovery time as fatigue levels increased. This study quantitatively demonstrates through wearable sensor data that, as fatigue levels increase, workers experience decreased body stability and longer recovery times. The findings of this study can inform individual worker fatigue management in the future. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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24 pages, 28075 KiB

Open AccessArticle

The Development of a New Vagus Nerve Simulation Electroceutical to Improve the Signal Attenuation in a Living Implant Environment

by Daeil Jo, Hyunung Lee, Youlim Jang, Paul Oh and Yongjin Kwon

Sensors 2024, 24(10), 3172; https://doi.org/10.3390/s24103172 - 16 May 2024

Viewed by 1807

Abstract

An electroceutical is a medical device that uses electrical signals to control biological functions. It can be inserted into the human body as an implant and has several crucial advantages over conventional medicines for certain diseases. This research develops a new vagus nerve simulation (VNS) electroceutical through an innovative approach to overcome the communication limitations of existing devices. A phased array antenna with a better communication performance was developed and applied to the electroceutical prototype. In order to effectively respond to changes in communication signals, we developed the steering algorithm and firmware, and designed the smart communication protocol that operates at a low power that is safe for the patients. This protocol is intended to improve a communication sensitivity related to the transmission and reception distance. Based on this technical approach, the heightened effectiveness and safety of the prototype have been ascertained, with the actual clinical tests using live animals. We confirmed the signal attenuation performance to be excellent, and a smooth communication was achieved even at a distance of 7 m. The prototype showed a much wider communication range than any other existing products. Through this, it is conceivable that various problems due to space constraints can be resolved, hence presenting many benefits to the patients whose last resort to the disease is the VNS electroceutical. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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24 pages, 1134 KiB

Open AccessArticle

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT

by Sara Caramaschi, Carl Magnus Olsson, Elizabeth Orchard, Jackson Molloy and Dario Salvi

Sensors 2024, 24(8), 2632; https://doi.org/10.3390/s24082632 - 20 Apr 2024

Cited by 2 | Viewed by 1179

Abstract

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users’ previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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20 pages, 2753 KiB

Open AccessArticle

A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment

by Sylvain Jung, Nicolas de l’Escalopier, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin and Damien Ricard

Sensors 2023, 23(8), 4000; https://doi.org/10.3390/s23084000 - 14 Apr 2023

Cited by 2 | Viewed by 2809

Abstract

This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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