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Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions...
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Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 97386

Special Issue Editors

Prof. Dr. Gaetano D. Gargiulo

E-Mail Website
Guest Editor
School of Engineering, Design and Built Environment, Western Sydney University, Milperra, NSW 2214, Australia
Interests: biomedical signal processing; wearable and electrode-less physiological monitoring; brain–computer interface; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Bifulco

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21 80125 Napoli, Italy
Interests: biomedical instrumentation; biomedical signal processing; biomedical image processing; clinical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Tara Julia Hamilton

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology, Sydney (UTS), Broadway, NSW 2007, Australia
Interests: biomedical engineering; neuromorphic engineering; mixed-signal integrated circuit design; medical devices; machine learning; circuits and systems for implantable and wearable biomedical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last couple of decades, wearable and personal technologies have presented new scenarios for patient monitoring. These technologies do not only allow classical monitoring of vital signs but provide much broader information on subject's movements, activities, lifestyles, and so on. Wearable devices often integrate many different sensors or take advantage of smartphones for their embedded sensors, computational power, and telecommunication services. Unconventional, non-obstructive, and smart sensing applications have seen extraordinary growth in recent years along with Internet of Things (IoT) healthcare solutions. Additionally, ubiquitous telecommunications facilities have allowed telemedicine and home-care applications to become increasingly pervasive.

This Special Issue aims to explore new solutions in this vast emerging scenario, contributions that address but are not restricted to the following topics are welcome:

  • Wearable patient sensors;
  • Smart clothing/textiles technologies;
  • Smart-phone applications for patient monitoring;
  • Patient activity monitoring;
  • Internet of Things (IoT) healthcare solutions;
  • Smart-home appliances and domotics for patient monitoring;
  • Pervasive and un-obstructive patient monitoring solutions;
  • Contact-less sensing;
  • Sensor for telemedicine;
  • Monitoring of elderly and disabled people;
  • Home-care sensing and the integration of medical records;
  • Body sensors networks;
  • Sensors for fitness and wellbeing;
  • Non-conventional patient monitoring;
  • The integration of multiple sensors information;
  • Continuous sensing data synthesis.

Submitted papers should present novel contributions and innovative applications. Relevant topical reviews are also welcome.

Dr. Gaetano D. Gargiulo
Prof. Paolo Bifulco
Dr. Tara J. Hamilton
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

  • smart and novel patient monitoring
  • wearable sensors
  • continuous and pervasive patient monitoring
  • cloud-based patient monitoring
  • Internet of Things for health
  • smart-phone health applications

Published Papers (16 papers)

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14 pages, 787 KiB  
Article
The Sensor Hub for Detecting the Developmental Characteristics in Reading in Children on a White vs. Colored Background/Colored Overlays
by Tamara Jakovljević, Milica M. Janković, Andrej M. Savić, Ivan Soldatović, Petar Todorović, Tadeja Jere Jakulin, Gregor Papa and Vanja Ković
Sensors 2021, 21(2), 406; https://doi.org/10.3390/s21020406 - 08 Jan 2021
Cited by 7 | Viewed by 3261
Abstract
This study investigated the influence of white vs. 12 background and overlay colors on the reading process in twenty-four school-age children. Previous research reported that colors could affect reading skills as an important factor in the emotional and physiological state of the body. [...] Read more.
This study investigated the influence of white vs. 12 background and overlay colors on the reading process in twenty-four school-age children. Previous research reported that colors could affect reading skills as an important factor in the emotional and physiological state of the body. The aim of the study was to assess developmental differences between second and third grade students of an elementary school, and to evaluate differences in electroencephalography (EEG), ocular, electrodermal activities (EDA) and heart rate variability (HRV). Our findings showed a decreasing trend with age regarding EEG power bands (Alpha, Beta, Delta, Theta) and lower scores of reading duration and eye-tracking measures in younger children compared to older children. As shown in the results, HRV parameters showed higher scores in 12 background and overlay colors among second than third grade students, which is linearly correlated to the level of stress and is readable from EDA measures as well. Our study showed the calming effect on second graders of turquoise and blue background colors. Considering other colors separately for each parameter, we assumed that there are no systematic differences in reading duration, EEG power band, eye-tracking and EDA measures. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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22 pages, 8044 KiB  
Article
Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction
by Timibloudi Enamamu, Abayomi Otebolaku, Jims Marchang and Joy Dany
Sensors 2020, 20(19), 5690; https://doi.org/10.3390/s20195690 - 06 Oct 2020
Cited by 10 | Viewed by 3793
Abstract
The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and [...] Read more.
The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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12 pages, 1791 KiB  
Communication
WCTECGdb: A 12-Lead Electrocardiography Dataset Recorded Simultaneously with Raw Exploring Electrodes’ Potential Directly Referred to the Right Leg
by Hossein Moeinzadeh, Joseph Assad, Paolo Bifulco, Mario Cesarelli, Aiden O’Loughlin, Jonathan C. Tapson, Ibrahim M. Shugman, Aravinda Thiagalingam and Gaetano D. Gargiulo
Sensors 2020, 20(11), 3275; https://doi.org/10.3390/s20113275 - 08 Jun 2020
Cited by 1 | Viewed by 6880
Abstract
With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, [...] Read more.
With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, comprises of 540 ten second segments recorded from 92 patients at Campbelltown Hospital, NSW Australia, and is now available for download from the Physionet platform. The data included in the dataset confirm that the Wilson’s Central Terminal (WCT) has a relatively large amplitude (up to 247% of lead II) with standard ECG characteristics such as a p-wave and a t-wave, and is highly variable during the cardiac cycle. As further examples of application for our data, we assess: (1) the presence of a conductive pathway between the legs and the heart concluding that in some cases is electrically significant and (2) the initial assumption about the limbs potential stating the dominance of the left arm concluding that this is not always the case and that might requires case to case assessment. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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13 pages, 3774 KiB  
Article
Sensor Positioning Influences the Accuracy of Knee Rom Data of an E-Rehabilitation System: A Preliminary Study with Healthy Subjects
by Carlos J. Marques, Christian Bauer, Dafne Grimaldo, Steffen Tabeling, Timo Weber, Alexander Ehlert, Alexandre H. Mendes, Juergen Lorenz and Frank Lampe
Sensors 2020, 20(8), 2237; https://doi.org/10.3390/s20082237 - 15 Apr 2020
Cited by 6 | Viewed by 2920
Abstract
E-rehabilitation is the term used to define medical rehabilitation programs that are implemented at home with the use of information and communication technologies. The aim was to test whether sensor position and the sitting position of the patient influence the accuracy of knee [...] Read more.
E-rehabilitation is the term used to define medical rehabilitation programs that are implemented at home with the use of information and communication technologies. The aim was to test whether sensor position and the sitting position of the patient influence the accuracy of knee range of movement (ROM) data displayed by the BPMpathway e-rehabilitation system. A preliminary study was conducted in a laboratory setting with healthy adults. Knee ROM data was measured with the BPMpathway e-rehabilitation system and simultaneously with a BIOPAC twin-axis digital goniometer. The main outcome was the root mean squared error (RMSE). A 20% increase or reduction in sitting height led to a RMSE increase. A ventral shift of the BPMpathway sensor by 45° and 90° caused significant measurement errors. A vertical shift was associated with a diminution of the measurement errors. The lowest RMSE (2.4°) was achieved when the sensor was placed below the knee. The knee ROM data measured by the BPMpathway system is comparable to the data of the concurrent system, provided the instructions of the manufacturer are respected concerning the sitting position of the subject for knee exercises, and disregarding the same instructions for sensor positioning, by placing the sensor directly below the knee. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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27 pages, 6107 KiB  
Article
Continuous Vital Monitoring During Sleep and Light Activity Using Carbon-Black Elastomer Sensors
by Titus Jayarathna, Gaetano D. Gargiulo and Paul P. Breen
Sensors 2020, 20(6), 1583; https://doi.org/10.3390/s20061583 - 12 Mar 2020
Cited by 16 | Viewed by 5848
Abstract
The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. [...] Read more.
The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed “VitalCore”, a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with “interfacing” material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9–100% accuracy in respiratory peak detection. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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17 pages, 4280 KiB  
Article
Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor
by Astrid García Patiño, Mahta Khoshnam and Carlo Menon
Sensors 2020, 20(3), 905; https://doi.org/10.3390/s20030905 - 08 Feb 2020
Cited by 33 | Viewed by 5289
Abstract
Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to [...] Read more.
Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to maintain better body posture during the activities of daily living. With the goal of preventing LBP through objective monitoring of back posture, this paper proposes a wireless, comfortable, and compact textile-based wearable platform to track trunk movements when the user bends forward. The smart garment developed for this purpose was prototyped with an inductive sensor formed by sewing a copper wire into an elastic fabric in a zigzag pattern. The results of an extensive simulation study showed that this unique design increases the inductance value of the sensor, and, consequently, improves its resolution. Furthermore, experimental evaluation on a healthy participant confirmed that the proposed wearable system with the suggested sensor design can easily detect forward bending movements. The evaluation scenario was then extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. Results of the magnetic interference test showed that, most notably, moving a cellphone towards the unworn prototype affects sensor readings, however, manipulating a cellphone, when wearing the prototype, did not affect the capability of the sensor in detecting forward bends. The proposed platform is a promising step toward developing wearable systems to monitor back posture in order to prevent or treat LBP. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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16 pages, 3078 KiB  
Article
Integrating Interactive Clothing and Cyber-Physical Systems: A Humanistic Design Perspective
by Weizhen Wang, Yuan Fang, Yukari Nagai, Dong Xu and Tsutomu Fujinami
Sensors 2020, 20(1), 127; https://doi.org/10.3390/s20010127 - 24 Dec 2019
Cited by 9 | Viewed by 3644
Abstract
This study is aimed at bridging the gap from a transdisciplinary perspective between cyber-physical systems (CPS) architecture in the field of information science and emotional design in the field of humanistic science for interactive fashion innovation. Information related to a familiar feeling in [...] Read more.
This study is aimed at bridging the gap from a transdisciplinary perspective between cyber-physical systems (CPS) architecture in the field of information science and emotional design in the field of humanistic science for interactive fashion innovation. Information related to a familiar feeling in the process of interactive clothing design is used to explain how the transformation could be realized from data. By creating the cyber-physical-clothing systems (CPCS), the architecture model in the hyper world and takes the development process of an interactive parent-child clothing as a case study for analyzing the transformation from the physical signal input to the social symbol recognition output. The experimental results, which from the perspective of clothing art design rather than information discipline, show that interactive parent-child clothing is not only suitable for the rehabilitation of autistic children but also recognized by most parents. The reasonable embedding of sensing technology can greatly enhance the added value of clothing products. This study provides a fruitful practical application reference for designers who are engaged in the field of art and design but not familiar with the relevant information technology. Furthermore, the application principle and the technical process of CPCS for further interactive clothing design is explained. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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35 pages, 18136 KiB  
Article
Characterisation of Morphic Sensors for Body Volume and Shape Applications
by Sami El Arja, Titus Jayarathna, Ganesh Naik, Paul Breen and Gaetano Gargiulo
Sensors 2020, 20(1), 90; https://doi.org/10.3390/s20010090 - 22 Dec 2019
Cited by 4 | Viewed by 3919
Abstract
Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to [...] Read more.
Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of >3 mm show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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9 pages, 19301 KiB  
Article
Self-Powered Smart Insole for Monitoring Human Gait Signals
by Wei Wang, Junyi Cao, Jian Yu, Rong Liu, Chris R. Bowen and Wei-Hsin Liao
Sensors 2019, 19(24), 5336; https://doi.org/10.3390/s19245336 - 04 Dec 2019
Cited by 22 | Viewed by 5652
Abstract
With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest [...] Read more.
With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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22 pages, 2003 KiB  
Article
Prediction of the Levodopa Challenge Test in Parkinson’s Disease Using Data from a Wrist-Worn Sensor
by Hamid Khodakarami, Lucia Ricciardi, Maria Fiorella Contarino, Rajesh Pahwa, Kelly E. Lyons, Victor J. Geraedts, Francesca Morgante, Alison Leake, Dominic Paviour, Andrea De Angelis and Malcolm Horne
Sensors 2019, 19(23), 5153; https://doi.org/10.3390/s19235153 - 25 Nov 2019
Cited by 29 | Viewed by 4278
Abstract
The response to levodopa (LR) is important for managing Parkinson’s Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the [...] Read more.
The response to levodopa (LR) is important for managing Parkinson’s Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson’s Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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15 pages, 2651 KiB  
Article
Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test
by Bor-Shing Lin, Ruei-Jie Jhang and Bor-Shyh Lin
Sensors 2019, 19(21), 4656; https://doi.org/10.3390/s19214656 - 26 Oct 2019
Cited by 9 | Viewed by 3867
Abstract
As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently [...] Read more.
As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently available to simultaneously record the real-time breathing sound, heart rhythm, and precise walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, a wearable and wireless multiparameter monitoring system is proposed to simultaneously monitor the breathing sound, oxygen saturation (SpO2), electrocardiograph (ECG) signals, and precise walking information during the 6MWT. Here, a wearable mechanical design was successfully used to reduce the effect of motion artifacts on the breathing sound and ECG signal. A multiparameter detection algorithm was designed to effectively estimate heart and breathing rates. Finally, the cardiopulmonary function of smokers was evaluated using the proposed system. The evaluation indicated that this system could reveal dynamic changes and differences in the breathing rate, heart rate, SpO2, walking speed, and acceleration during the 6MWT. The proposed system can serve as a more integrated approach to monitor cardiopulmonary parameters and obtain precise walking information simultaneously during the 6MWT. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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19 pages, 3005 KiB  
Article
Closing the Wearable Gap—Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements
by David Saucier, Tony Luczak, Phuoc Nguyen, Samaneh Davarzani, Preston Peranich, John E. Ball, Reuben F. Burch V, Brian K. Smith, Harish Chander, Adam Knight and R. K. Prabhu
Sensors 2019, 19(16), 3509; https://doi.org/10.3390/s19163509 - 10 Aug 2019
Cited by 23 | Viewed by 6261
Abstract
The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot–ankle complex. The purpose of this study was to continue the validation of SRS for joint angle [...] Read more.
The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot–ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements—plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)—were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot–ankle movement capture. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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19 pages, 1000 KiB  
Article
The Use of Data from the Parkinson’s KinetiGraph to Identify Potential Candidates for Device Assisted Therapies
by Hamid Khodakarami, Parisa Farzanehfar and Malcolm Horne
Sensors 2019, 19(10), 2241; https://doi.org/10.3390/s19102241 - 15 May 2019
Cited by 26 | Viewed by 4022
Abstract
Device-assisted therapies (DAT) benefit people with Parkinsons Disease (PwP) but many referrals for DAT are unsuitable or too late, and a screening tool to aid in identifying candidates would be helpful. This study aimed to produce such a screening tool by building a [...] Read more.
Device-assisted therapies (DAT) benefit people with Parkinsons Disease (PwP) but many referrals for DAT are unsuitable or too late, and a screening tool to aid in identifying candidates would be helpful. This study aimed to produce such a screening tool by building a classifier that models specialist identification of suitable DAT candidates. To our knowledge, this is the first objective decision tool for managing DAT referral. Subjects were randomly assigned to either a construction set (n = 112, to train, develop, cross validate, and then evaluate the classifier’s performance) or to a test set (n = 60 to test the fully specified classifier), resulting in a sensitivity and specificity of 89% and 86.6%, respectively. The classifier’s performance was then assessed in PwP who underwent deep brain stimulation (n = 31), were managed in a non-specialist clinic (n = 81) or in PwP in the first five years from diagnosis (n = 22). The classifier identified 87%, 92%, and 100% of the candidates referred for DAT in each of the above clinical settings, respectively. Furthermore, the classifier score changed appropriately when therapeutic intervention resolved troublesome fluctuations or dyskinesia that would otherwise have required DAT. This study suggests that information from objective measurement could improve timely referral for DAT. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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19 pages, 4577 KiB  
Article
A Comparison of Reflective Photoplethysmography for Detection of Heart Rate, Blood Oxygen Saturation, and Respiration Rate at Various Anatomical Locations
by Sally K. Longmore, Gough Y. Lui, Ganesh Naik, Paul P. Breen, Bin Jalaludin and Gaetano D. Gargiulo
Sensors 2019, 19(8), 1874; https://doi.org/10.3390/s19081874 - 19 Apr 2019
Cited by 85 | Viewed by 14482
Abstract
Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of [...] Read more.
Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm × 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO2), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO2 both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO2, however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest; however, no site was identified as capable of measuring all parameters while walking. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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12 pages, 1945 KiB  
Article
Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
by Mengxuan Li, Shanshan Tian, Linlin Sun and Xi Chen
Sensors 2019, 19(7), 1737; https://doi.org/10.3390/s19071737 - 11 Apr 2019
Cited by 29 | Viewed by 4941
Abstract
Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for [...] Read more.
Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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Review

Jump to: Research

30 pages, 1026 KiB  
Review
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation
by Nawadita Parajuli, Neethu Sreenivasan, Paolo Bifulco, Mario Cesarelli, Sergio Savino, Vincenzo Niola, Daniele Esposito, Tara J. Hamilton, Ganesh R. Naik, Upul Gunawardana and Gaetano D. Gargiulo
Sensors 2019, 19(20), 4596; https://doi.org/10.3390/s19204596 - 22 Oct 2019
Cited by 195 | Viewed by 17418
Abstract
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such [...] Read more.
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations. Full article
(This article belongs to the Special Issue Wearable, Smart, Pervasive, and Unconventional Sensing for Health Monitoring: Solutions from Today and for Tomorrow)
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