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Advance in Sensors and Sensing Systems for Healthcare and Convalescence

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

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 32340

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Special Issue Editors

Prof. Dr. Ting Li

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Guest Editor

1. Biomedical Engineering (BME) Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Baidi Road, Tianjin 300192, China
2. Electronics Science Technology College, University of Electronic Science and Technology of China, Chengdu 610051, China
Interests: optoelectronic sensors and sensing system; medical optoelectronics; device and instrumentation; optical imaging
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ji-Xin Cheng

E-Mail Website
Guest Editor

Boston University Photonics Center, 8 Saint Mary's Street, Boston, MA 02459, USA
Interests: molecular spectroscopic imaging technologies; label-free microscopy; medical photonics neurophotonics; cancer metabolism; photonics for infectious diseases

Special Issue Information

Dear Colleagues,

Following people’s awareness of the importance of health and the aging of the world population, innovative technologies for sensors for healthcare and convalescence have attracted more attention in the fields of materials and sensors, biomedical engineering, optoelectronics, instrumentation, computer science and intelligence, and robotics. From simple healthcare devices to intelligent healthcare sensing systems, accurate detection and early warning of health conditions for humans, interacting with different living scenarios represent the most important requirements. The innovative sensors, sensing technologies, and sensing system that are compatible or implantable for convalescence instruments or medical robots are facing increasing demand for dysfunctional and aging people.

The aim of this Special Issue is to collect recent advances on sensors and sensing systems for healthcare and convalescence applications. High-quality research articles, short communication, as well as reviews, are welcome. Of special interest is research work that seeks to address recent developments in small-size and wearable medical optoelectronic sensors or sensing systems, the fusion of these sensors into current health/medical devices and robots, biomedical sensor technology, reliability testing, novel strategies in handling issues in on-human applications, the state-of-the-art applications, innovative data analysis technologies on sensed data in the framework of health care and wellness, as well as relevant prospects in terms of opportunities and challenges.

Papers are solicited in, but are not limited to, the following and related topics:

New sensor materials and technologies for healthcare and convalescence
Printed, flexible, biodegradable and biocompatible optoelectronics
Sensor devices and sensor arrays
Novel optoelectronics for brain activity monitoring
Sensors and Systems for Brain Computer Interfaces
Sensors and Systems for Physical Rehabilitation
Wearable and implantable sensors for healthcare and convalescence
Application of the sensors in health/medical devices and robots
Reliability testing of above sensors and systems
Issues and strategies in on-human application of the above sensors
Innovative data analysis technologies in the above scope

Prof. Dr. Ting Li
Prof. Dr. Ji-Xin Cheng
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.

Published Papers (7 papers)

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Research

18 pages, 22341 KiB

Open AccessArticle

Magnetometer-Based Drift Correction During Rest in IMU Arm Motion Tracking

by Frieder Wittmann, Olivier Lambercy and Roger Gassert

Sensors 2019, 19(6), 1312; https://doi.org/10.3390/s19061312 - 15 Mar 2019

Cited by 44 | Viewed by 7643

Abstract

Real-time motion capture of the human arm in the home environment has many use cases, such as video game and therapy applications. The required tracking can be based on off-the-shelf Inertial Measurement Units (IMUs) with integrated three-axis accelerometers, gyroscopes, and magnetometers. However, this usually requires a homogeneous magnetic field to correct for orientation drift, which is often not available inside buildings. In this paper, RPMC (Rest Pose Magnetometer-based drift Correction), a novel method that is robust to long term drift in environments with inhomogeneous magnetic fields, is presented. The sensor orientation is estimated by integrating the angular velocity measured by the gyroscope and correcting drift around the pitch and roll axes with the acceleration information. This commonly leads to short term drift around the gravitational axis. Here, during the calibration phase, the local magnetic field direction for each sensor, and its orientation relative to the inertial frame, are recorded in a rest pose. It is assumed that arm movements in free space are exhausting and require regular rest. A set of rules is used to detect when the user has returned to the rest pose, to then correct for the drift that has occurred with the magnetometer. Optical validations demonstrated accurate (root mean square error

R M S = 6.1

°), low latency (

61 m s

) tracking of the user’s wrist orientation, in real time, for a full hour of arm movements. The reduction in error relative to three alternative methods implemented for comparison was between

82.5 %

and

90.7 %

for the same movement and environment. Therefore, the proposed arm tracking method allows for the correction of orientation drift in an inhomogeneous magnetic field by exploiting the user’s need for frequent rest. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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14 pages, 3602 KiB

Open AccessArticle

Acoustic Stimulation by Shunt-Diode Pre-Linearizer Using Very High Frequency Piezoelectric Transducer for Cancer Therapeutics

by Hojong Choi and Se-woon Choe

Sensors 2019, 19(2), 357; https://doi.org/10.3390/s19020357 - 16 Jan 2019

Cited by 24 | Viewed by 3366

Abstract

In this paper, we proposed cancer cell acoustic stimulation by shunt-diode pre-linearizer scheme using a very high frequency (≥100 MHz) piezoelectric transducer. To verify the concept of our proposed scheme, we performed pulse-echo detection, and accessed therapeutic effects of human cervical cancer cells exposed to acoustic stimulation experiments using 100 MHz focused piezoelectric transducer triggered by PA with and without the proposed shunt-diode pre-linearizer scheme. In the pulse-echo detection responses, the peak-to-peak voltage of the echo signal when using the PA with shunt-diode pre-linearizer (49.79 mV) was higher than that when using the PA alone (29.87 mV). In the experimental results, the cell densities of cancer cells on Day 4 when using no acoustic stimulation (control group), the very high-frequency piezoelectric transducer triggered by PA only and PA combined with proposed pre-linearizer schemes (1 V and 5 V DC bias voltages) showed 100%, 92.8 ± 4.2%, 84.2 ± 4.6%, and 78 ± 2.9%, respectively. Therefore, we confirmed that the shunt-diode pre-linearizer could improve the performances of the pulse signals of the PA, thus, enabling better therapeutic stimulation performances for cancer cell suppression. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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15 pages, 2328 KiB

Open AccessArticle

Design and Implementation of Respiration Rate Measurement System Using an Information Filter on an Embedded Device

by Radius Bhayu Prasetiyo, Kyu-Sang Choi and Gi-Hun Yang

Sensors 2018, 18(12), 4208; https://doi.org/10.3390/s18124208 - 30 Nov 2018

Cited by 4 | Viewed by 3528

Abstract

In this work, an algorithm was developed to measure respiration rate for an embedded device that can be used by a field robot for relief operations. With this algorithm, the rate measurement was calculated based on direct influences of respiratory-induced intensity variation (RIIV) on blood flow in cardiovascular pathways. For this, a photoplethysmogram (PPG) sensor was used to determine changes in heartbeat frequencies. The PPG sensor readings were filtered using an Information Filter and a fast Fourier transform (FFT) to determine the state of RIIV. With a relatively light initialization, the information filter can estimate unknown variables based on a series of measurements containing noise and other inaccuracies. Therefore, this filter is suitable for application in an embedded device. For faster calculation time in the implementation, the FFT analysis was calculated only for a major peak in frequency domain. Test and measurement of respiration rate was conducted based on the device algorithm and spirometer. Heartbeat measurements were also evaluated by comparing the heartbeat data of the PPG sensor and pulse-oximeter. Based on the test, the implemented algorithm can measure the respiration rate with approximately 80% accuracy compared with the spirometer. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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18 pages, 1756 KiB

Open AccessArticle

Optimizing Wavelet ECG Watermarking to Maintain Measurement Performance According to Industrial Standard

by Agnieszka Świerkosz and Piotr Augustyniak

Sensors 2018, 18(10), 3401; https://doi.org/10.3390/s18103401 - 11 Oct 2018

Cited by 9 | Viewed by 2699

Abstract

Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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18 pages, 2295 KiB

Open AccessArticle

Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment

by Guangying Pei, Jinglong Wu, Duanduan Chen, Guoxin Guo, Shuozhen Liu, Mingxuan Hong and Tianyi Yan

Sensors 2018, 18(10), 3396; https://doi.org/10.3390/s18103396 - 11 Oct 2018

Cited by 29 | Viewed by 6564

Abstract

Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system’s operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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11 pages, 3423 KiB

Open AccessArticle

Accelerometric Trunk Sensors to Detect Changes of Body Positions in Immobile Patients

by Katrin Rauen, Judith Schaffrath, Cauchy Pradhan, Roman Schniepp and Klaus Jahn

Sensors 2018, 18(10), 3272; https://doi.org/10.3390/s18103272 - 28 Sep 2018

Cited by 7 | Viewed by 3126

Abstract

Mobilization, verticalization and position change are mandatory for severely affected neurological patients in early neurorehabilitation in order to improve neurological status and prevent complications. However, with the exception of hospitals and rehabilitation facilities, this activity is not usually monitored and so far the automated monitoring of position changes in immobile patients has not been investigated. Therefore, we investigated whether accelerometers on the upper trunk could reliably detect body position changes in immobile patients. Thirty immobile patients in early neurorehabilitation (Barthel Index ≤ 30) were enrolled. Two tri-axial accelerometers were placed on the upper trunk and on the thigh. Information on the position and position changes of the subject were derived from accelerometer data and compared to standard written documentation in the hospital over 24 h. Frequency and duration of different body positions (supine, sidelying, sitting) were measured. Data are presented as mean ± SEM. Groups were compared using one-way ANOVA or Kruskal-Wallis-test. Differences were considered significant if p < 0.05. Trunk sensors detected 100% and thigh sensors 66% of position changes (p = 0.0004) compared to standard care documentation. Furthermore, trunk recording also detected additional spontaneous body position changes that were not documented in standard care (81.8 ± 4.4% of all position changes were documented in standard care documentation) (p < 0.0001). We found that accelerometric trunk sensors are suitable for recording position changes and mobilization of severely affected patients. Our findings suggest that using accelerometers for care documentation is useful for monitoring position changes and mobilization frequencies in and outside of hospital for severely affected neurological patients. Accelerometric sensors may be valuable in monitoring continuation of care plans after intensive neurorehabilitation. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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13 pages, 4295 KiB

Open AccessArticle

HemoKinect: A Microsoft Kinect V2 Based Exergaming Software to Supervise Physical Exercise of Patients with Hemophilia

by Fernando Mateo, Emilio Soria-Olivas, Juan J. Carrasco, Santiago Bonanad, Felipe Querol and Sofía Pérez-Alenda

Sensors 2018, 18(8), 2439; https://doi.org/10.3390/s18082439 - 26 Jul 2018

Cited by 24 | Viewed by 4893

Abstract

Patients with hemophilia need to strictly follow exercise routines to minimize their risk of suffering bleeding in joints, known as hemarthrosis. This paper introduces and validates a new exergaming software tool called HemoKinect that intends to keep track of exercises using Microsoft Kinect V2’s body tracking capabilities. The software has been developed in C++ and MATLAB. The Kinect SDK V2.0 libraries have been used to obtain 3D joint positions from the Kinect color and depth sensors. Performing angle calculations and center-of-mass (COM) estimations using these joint positions, HemoKinect can evaluate the following exercises: elbow flexion/extension, knee flexion/extension (squat), step climb (ankle exercise) and multi-directional balance based on COM. The software generates reports and progress graphs and is able to directly send the results to the physician via email. Exercises have been validated with 10 controls and eight patients. HemoKinect successfully registered elbow and knee exercises, while displaying real-time joint angle measurements. Additionally, steps were successfully counted in up to 78% of the cases. Regarding balance, differences were found in the scores according to the difficulty level and direction. HemoKinect supposes a significant leap forward in terms of exergaming applicability to rehabilitation of patients with hemophilia, allowing remote supervision. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Healthcare and Convalescence)

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