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Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

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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 (31 July 2021) | Viewed by 48414

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Special Issue Editors

Dr. Maria de Fátima Domingues

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Guest Editor

1. Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
2. Instituto de Telecomunicações, Aveiro, Portugal
Interests: optical fiber sensors; fiber Bragg gratings; Fabry-Perot interformetric sensors; eHealth applications; gait analysis; wearable sensing devices
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Sciarrone

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Co-Guest Editor

DITEN, University of Genoa, Genoa, Italy
Interests: digital signal processing; eHealth applications; mobile computing
Special Issues, Collections and Topics in MDPI journals

Dr. Ayman Radwan

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Co-Guest Editor

Instituto de Telecomunicações and Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: wireless communications; radio resource management; Internet of Things; future generations of mobile communications (5G)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demographic shift of the population towards an increase in the number of elder citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be aided by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the cost in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data relies on the sensors; hence, when considering wearable and BAN sensing integration, they must prove to be accurate and reliable solutions.

This Special Issue will focus on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. It will cover novel technological achievements related to different sensing technologies (optical or electronic), their design, and implementation. Both original research papers and review articles describing the current state-of-the-art are welcome. We hope this SI will provide you with an overview of the present status and future outlook of the aforementioned topics.

The manuscripts should cover but need not be limited to the following topics:

Optical fiber sensing of physiological parameters;
Wearable biomedical sensors;
Optical fiber non-invasive devices;
Optical fiber sensors in e-Health architectures;
Body area network sensors (BANs);
Energy efficient eHealth architectures;
Big data analysis for eHealth;
Sensors for physical rehabilitation;
Innovative materials for sensing design;
Advanced signal processing techniques;
Applications including, but not limited to physical rehabilitation, robotics, medical diagnostics and therapy, and cardiovascular and pulmonary rehabilitation.

Dr. Maria de Fátima Domingues
Dr. Andrea Sciarrone
Dr. Ayman Radwan
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

optical fiber sensors
biomedical sensors
wearable sensors
e-Health
body-area-network sensors
smart health

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 195 KiB

Open AccessEditorial

Special Issue “Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures”

by Maria de Fátima Domingues, Andrea Sciarrone and Ayman Radwan

Sensors 2021, 21(24), 8509; https://doi.org/10.3390/s21248509 - 20 Dec 2021

Viewed by 2302

Abstract

The demographic shift of the population toward an increased number of elder citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population [...] Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

Research

Jump to: Editorial, Review

16 pages, 10947 KiB

Open AccessArticle

A Smart Walker for People with Both Visual and Mobility Impairment

by Nafisa Mostofa, Christopher Feltner, Kelly Fullin, Jonathan Guilbe, Sharare Zehtabian, Salih Safa Bacanlı, Ladislau Bölöni and Damla Turgut

Sensors 2021, 21(10), 3488; https://doi.org/10.3390/s21103488 - 17 May 2021

Cited by 14 | Viewed by 6740

Abstract

In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs of this population. We investigated different sensing technologies (ultrasound-based, infrared depth cameras and RGB cameras with advanced computer vision processing), software configurations, and user interface modalities (haptic and audio signal based). Our experiments show that there are several engineering choices that can be used in the design of such assistive devices. Furthermore, we found that a holistic evaluation of the end-to-end performance of the systems is necessary, as the quality of the user interface often has a larger impact on the overall performance than increases in the sensing accuracy beyond a certain point. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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10 pages, 1217 KiB

Open AccessCommunication

Validity of Hip and Ankle Worn Actigraph Accelerometers for Measuring Steps as a Function of Gait Speed during Steady State Walking and Continuous Turning

by Lucian Bezuidenhout, Charlotte Thurston, Maria Hagströmer and David Moulaee Conradsson

Sensors 2021, 21(9), 3154; https://doi.org/10.3390/s21093154 - 01 May 2021

Cited by 19 | Viewed by 2693

Abstract

This study aimed to investigate the accuracy and reliability of hip and ankle worn Actigraph GT3X+ (AG) accelerometers to measure steps as a function of gait speed. Additionally, the effect of the low frequency extension filter (LFEF) on the step accuracy was determined. Thirty healthy individuals walked straight and walked with continuous turns in different gait speeds. Number of steps were recorded with a hip and ankle worn AG, and with a Stepwatch (SW) activity monitor positioned around the right ankle, which was used as a reference for step count. The percentage agreement, interclass correlation coefficients and Bland–Altmann plots were determined between the AG and the reference SW across gait speeds for the two walking conditions. The ankle worn AG with the default filter was the most sensitive for step detection at >0.6 m/s, whilst accurate step detection for gait speeds < 0.6 m/s were only observed when applying the LFEF. The hip worn AG with the default filter showed poor accuracy (12–78%) at gait speeds < 1.0 m/s whereas the accuracy increased to >87% for gait speeds < 1.0 m/s when applying the LFEF. Ankle worn AG was the most sensitive to measure steps at a vast range of gait speeds. Our results suggest that sensor placement and filter settings need to be taken into account to provide accurate estimates of step counts. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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20 pages, 3888 KiB

Open AccessArticle

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

by Joshua Di Tocco, Luigi Raiano, Riccardo Sabbadini, Carlo Massaroni, Domenico Formica and Emiliano Schena

Sensors 2021, 21(9), 3018; https://doi.org/10.3390/s21093018 - 25 Apr 2021

Cited by 24 | Viewed by 3225

Abstract

The continuous and simultaneous monitoring of physiological parameters represents a key aspect in clinical environments, remote monitoring and occupational settings. In this regard, respiratory rate (RR) and heart rate (HR) are correlated with several physiological and pathological conditions of the patients/workers, and with environmental stressors. In this work, we present and validate a wearable device for the continuous monitoring of such parameters. The proposed system embeds four conductive sensors located on the user’s chest which allow retrieving the breathing activity through their deformation induced during cyclic expansion and contraction of the rib cage. For monitoring HR we used an embedded IMU located on the left side of the chest wall. We compared the proposed device in terms of estimating HR and RR against a reference system in three scenarios: sitting, standing and supine. The proposed system reliably estimated both RR and HR, showing low error averaged along subjects in all scenarios. This is the first study focused on the feasibility assessment of a wearable system based on a multi-sensor configuration (i.e., conductive sensors and IMU) for RR and HR monitoring. The promising results encourage the application of this approach in clinical and occupational settings. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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20 pages, 4997 KiB

Open AccessArticle

Short- and Long-Term Effects of a Scapular-Focused Exercise Protocol for Patients with Shoulder Dysfunctions—A Prospective Cohort

by Cristina dos Santos, Mark A. Jones and Ricardo Matias

Sensors 2021, 21(8), 2888; https://doi.org/10.3390/s21082888 - 20 Apr 2021

Cited by 7 | Viewed by 4276

Abstract

Current clinical practice lacks consistent evidence in the management of scapular dyskinesis. This study aims to determine the short- and long-term effects of a scapular-focused exercise protocol facilitated by real-time electromyographic biofeedback (EMGBF) on pain and function, in individuals with rotator cuff related pain syndrome (RCS) and anterior shoulder instability (ASI). One-hundred and eighty-three patients were divided into two groups (n = 117 RCS and n = 66 ASI) and guided through a structured exercise protocol, focusing on scapular dynamic control. Values of pain and function (shoulder pain and disability index (SPADI) questionnaire, complemented by the numeric pain rating scale (NPRS) and disabilities of the arm, shoulder, and hand (DASH) questionnaire) were assessed at the initial, 4-week, and 2-year follow-up and compared within and between. There were significant differences in pain and function improvement between the initial and 4-week assessments. There were no differences in the values of DASH 1st part and SPADI between the 4-week and 2-year follow-up. There were no differences between groups at the baseline and long-term, except for DASH 1st part and SPADI (p < 0.05). Only 29 patients (15.8%) had a recurrence episode at follow-up. These results provide valuable information on the positive results of the protocol in the short- and long-term. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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18 pages, 3225 KiB

Open AccessEditor’s ChoiceArticle

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

by Kayisan M. Dalmeida and Giovanni L. Masala

Sensors 2021, 21(8), 2873; https://doi.org/10.3390/s21082873 - 19 Apr 2021

Cited by 65 | Viewed by 7104

Abstract

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence, the study will be mainly focusing on heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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13 pages, 5011 KiB

Open AccessArticle

Monitoring Physical Activity with a Wearable Sensor in Patients with COPD during In-Hospital Pulmonary Rehabilitation Program: A Pilot Study

by Sebastian Rutkowski, Joren Buekers, Anna Rutkowska, Błażej Cieślik and Jan Szczegielniak

Sensors 2021, 21(8), 2742; https://doi.org/10.3390/s21082742 - 13 Apr 2021

Cited by 10 | Viewed by 3698

Abstract

Accelerometers have become a standard method of monitoring physical activity in everyday life by measuring acceleration in one, two, or three axes. These devices provide reliable and objective measurements of the duration and intensity of physical activity. We aimed to investigate whether patients undertake physical activity during non-supervised days during stationary rehabilitation and whether patients adhere to the rigor of 24 h monitoring. The second objective was to analyze the strengths and weaknesses of such kinds of sensors. The research enrolled 13 randomly selected patients, qualified for in-patient, 3 week, high-intensity, 5 times a week pulmonary rehabilitation. The SenseWear armband was used for the assessment of physical activity. Participants wore the device 24 h a day for the next 4 days (Friday–Monday). The analysis of the number of steps per day, the time spent lying as well as undertaking moderate or vigorous physical activity (>3 metabolic equivalents of task (METs)), and the energy expenditure expressed in kcal showed no statistically significant difference between the training days and the days off. It seems beneficial to use available physical activity sensors in patients with chronic obstructive pulmonary disease (COPD); measurable parameters provide feedback that may increase the patient’s motivation to be active to achieve health benefits. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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23 pages, 3737 KiB

Open AccessArticle

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors

by Philip Boyer, David Burns and Cari Whyne

Sensors 2021, 21(5), 1669; https://doi.org/10.3390/s21051669 - 01 Mar 2021

Cited by 22 | Viewed by 4088

Abstract

Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for most ML algorithms, especially deep learning models that are prone to overconfident predictions based on in-distribution (IIN) classes. To simulate the OOD problem in physiotherapy, our team collected a new dataset (SPARS9x) consisting of inertial data captured by smartwatches worn by 20 healthy subjects as they performed supervised physiotherapy exercises (IIN), followed by a minimum 3 h of data captured for each subject as they engaged in unrelated and unstructured activities (OOD). In this paper, we experiment with three traditional algorithms for OOD-detection using engineered statistical features, deep learning-generated features, and several popular deep learning approaches on SPARS9x and two other publicly-available human activity datasets (MHEALTH and SPARS). We demonstrate that, while deep learning algorithms perform better than simple traditional algorithms such as KNN with engineered features for in-distribution classification, traditional algorithms outperform deep learning approaches for OOD detection for these HAR time series datasets. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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24 pages, 6217 KiB

Open AccessArticle

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

by Long Liu, Huihui Wang, Haorui Li, Jiayi Liu, Sen Qiu, Hongyu Zhao and Xiangyang Guo

Sensors 2021, 21(4), 1347; https://doi.org/10.3390/s21041347 - 14 Feb 2021

Cited by 24 | Viewed by 3880

Abstract

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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18 pages, 8481 KiB

Open AccessArticle

Paddle Stroke Analysis for Kayakers Using Wearable Technologies

by Long Liu, Hui-Hui Wang, Sen Qiu, Yun-Cui Zhang and Zheng-Dong Hao

Sensors 2021, 21(3), 914; https://doi.org/10.3390/s21030914 - 29 Jan 2021

Cited by 20 | Viewed by 4390

Abstract

Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers’ motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers’ actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker’s motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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Review

Jump to: Editorial, Research

26 pages, 495 KiB

Open AccessReview

Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications

by Pedro H. Vilela, Joel J. P. C. Rodrigues, Rodrigo da R. Righi, Sergei Kozlov and Vinicius F. Rodrigues

Sensors 2020, 20(9), 2553; https://doi.org/10.3390/s20092553 - 30 Apr 2020

Cited by 28 | Viewed by 4634

Abstract

Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed. Full article

(This article belongs to the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures)

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