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Sensors toward Unobtrusive Health Monitoring II

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 41187

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Special Issue Editors

Prof. Dr. Thomas M. Deserno

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Guest Editor

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
Interests: accident and emergency informatics; continuous health monitoring; smart car; smart home; biomedical image and signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Christian Baumgartner

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Guest Editor

Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, 8010 Graz, Austria
Interests: biomedical sensors and signals; sensors in medical devices and IVDs; medical device regulation & safety
Special Issues, Collections and Topics in MDPI journals

Prof. Steffen Leonhardt

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Guest Editor

Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, D-52074 Aachen, Germany
Interests: physiological measurement techniques; personal health care systems and feedback control systems in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, medicine, diagnostics, and health systems are changing worldwide. A few years ago, physicians performed a medical assessment or diagnostics only when symptoms had already occurred, and then only in their professional locations (residency or hospital). Driven by devices for sports and wellbeing, continuous behavioral monitoring has been established and is currently transforming to the medical monitoring of vital signs and other individual health parameters. The disadvantage of behavioral monitoring devices is that you need to wear or carry them. In future, private environments such as cars or homes will support continuous health monitoring with rather unobtrusive sensors. The changing paradigm aims at predicting adverse health events in order to take actions to prevent them. Therefore, a great deal of research is being carried out in this field. We particularly focus this Special Issue on the following topics (if you are not sure whether your work fits within the Special Issue’s scope, please contact the Guest Editor):

Prof. Dr. Thomas Deserno
Prof. Dr. Christian Baumgartner
Prof. Steffen Leonhardt
Guest Editors

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Keywords

Sensors for mobile and unobtrusive continuous health monitoring
Indirect sensors combining artificial intelligence
Calibration and accuracy assessment of sensors
Data management for continuously sensing devices
Combination of behavioral, medical, and environmental sensors or sensor data
Medical applications of continuous sensing

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Published Papers (7 papers)

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Research

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20 pages, 4430 KiB

Open AccessArticle

Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking

by Mostafa Haghi, Arman Ershadi and Thomas M. Deserno

Sensors 2023, 23(4), 2066; https://doi.org/10.3390/s23042066 - 12 Feb 2023

Cited by 4 | Viewed by 2654

Abstract

The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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12 pages, 3198 KiB

Open AccessArticle

Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving

by Joana M. Warnecke, Nagarajan Ganapathy, Eugen Koch, Andreas Dietzel, Maximilian Flormann, Roman Henze and Thomas M. Deserno

Sensors 2022, 22(11), 4198; https://doi.org/10.3390/s22114198 - 31 May 2022

Cited by 10 | Viewed by 2985

Abstract

Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (−6.69 dB) and rural had the highest (−6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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16 pages, 1249 KiB

Open AccessArticle

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography

by Chuong Ngo, Carlos Munoz, Markus Lueken, Alfred Hülkenberg, Cornelius Bollheimer, Andrey Briko, Alexander Kobelev, Sergey Shchukin and Steffen Leonhardt

Sensors 2022, 22(5), 1941; https://doi.org/10.3390/s22051941 - 2 Mar 2022

Cited by 14 | Viewed by 5096

Abstract

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200

Ω

with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20–150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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19 pages, 5036 KiB

Open AccessArticle

Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning

by Nagarajan Ganapathy, Diana Baumgärtel and Thomas M. Deserno

Sensors 2021, 21(10), 3542; https://doi.org/10.3390/s21103542 - 19 May 2021

Cited by 21 | Viewed by 3694

Abstract

Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length

V

, word-size

W

, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with

W = 3

and

|V| = 3

yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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20 pages, 5957 KiB

Open AccessArticle

Car Seats with Capacitive ECG Electrodes Can Detect Cardiac Pacemaker Spikes

by Durmus Umutcan Uguz, Rosalia Dettori, Andreas Napp, Marian Walter, Nikolaus Marx, Steffen Leonhardt and Christoph Hoog Antink

Sensors 2020, 20(21), 6288; https://doi.org/10.3390/s20216288 - 4 Nov 2020

Cited by 15 | Viewed by 4392

Abstract

The capacitive electrocardiograph (cECG) has been tested for several measurement scenarios, including hospital beds, car seats and chairs since it was first proposed. The inferior signal quality of the cECG compared to the gold standard ECG guides the ongoing research in the direction of out-of-hospital applications, where unobtrusiveness is sought and high-level diagnostic signal quality is not essential. This study aims to expand the application range of cECG not in terms of the measurement scenario but in the profile of the subjects by including subjects with implanted cardiac pacemakers. Within this study, 20 patients with cardiac pacemakers were recruited during their clinical device follow-up and cECG measurements were conducted using a seat equipped with integrated cECG electrodes. The multichannel cECG recordings of active unipolar and bipolar pacemaker stimulation were analyzed offline and evaluated in terms of

F_{β}

scores using a pacemaker spike detection algorithm.

F_{β}

scores from 3652 pacing events, varying from 0.62 to 0.78, are presented with influencing parameters in the algorithm and the comparison of cECG channels. By tuning the parameters of the algorithm, different ranges of

F_{β}

scores were found as 0.32 to 0.49 and 0.78 to 0.88 for bipolar and unipolar stimulations, respectively. For the first time, this study shows the feasibility of a cECG system allowing health monitoring in daily use on subjects wearing cardiac pacemakers. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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Review

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17 pages, 848 KiB

Open AccessReview

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

by Ju Wang, Joana M. Warnecke, Mostafa Haghi and Thomas M. Deserno

Sensors 2020, 20(9), 2442; https://doi.org/10.3390/s20092442 - 25 Apr 2020

Cited by 48 | Viewed by 6491

Abstract

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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Other

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37 pages, 5036 KiB

Open AccessSystematic Review

Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review

by Vinothini Selvaraju, Nicolai Spicher, Ju Wang, Nagarajan Ganapathy, Joana M. Warnecke, Steffen Leonhardt, Ramakrishnan Swaminathan and Thomas M. Deserno

Sensors 2022, 22(11), 4097; https://doi.org/10.3390/s22114097 - 28 May 2022

Cited by 40 | Viewed by 14885

Abstract

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO₂). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO₂, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO₂ are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO₂ is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO₂ monitoring. Full article

(This article belongs to the Special Issue Sensors toward Unobtrusive Health Monitoring II)

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