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Topical Collection "Wearable and Unobtrusive Monitoring Systems"

Editors

Collection Editor
Prof. Toshiyo Tamura

Future Robotics Organization, Waseda University, Tokyo, Japan
Website | E-Mail
Interests: bio instrumentation, bio signal interpretation, noninvasive monitor, unobtrusive sensing, blood pressure monitoring, healthcare system, assistive device, temperature regulation, heat stroke
Collection Editor
Prof. Wenxi Chen

Biomedical Information Technology Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan
Website | E-Mail
Interests: Bio-signal interpretation, noninvasive measurement, wearable sensor, ECG, HR, HRV analysis, deep body thermometry

Topical Collection Information

Dear Colleagues,

Wearable and unobtrusive monitoring systems are rapidly evolving and used for healthcare, exercise activity monitoring, performance assessment, and other fields such as the environmental, agricultural, and food industries.

Concomitantly with the development of biocompatible materials and IoT technologies,    the optimization of the sensing system has become more complex, ranging from biology, chemistry, nanotechnology, and micro-fabrication to wireless networks and electronic engineering.

The aims of this Topical Collection are as follows:

  • To present the current developments in wearable and unobtrusive monitoring including medical parameters such as cardiopulmonary, bioelectrical signal, motion and rehabilitation, temperature, blood substances, and nutrition estimation.
  • Recent developments in wearable and unobtrusive monitoring devices are more accurate than previous ones. The scope of monitoring will spread widely, among medically approved, precise devices as well as environmental and industrial monitoring including emergency and safety monitoring. Additionally, simple handling, fast response, low cost, and highly reliable devices are needed to improve quality of life. We welcome such device development, total monitoring system with wireless transmitting and storing data technologies, and security.
  • Both review articles and original research papers are welcome. We have a particular interest in papers concerning novel and innovative approaches in wearable and unobtrusive monitors, as well as total monitoring systems.

Prof. Toshiyo Tamura
Prof. Wenxi Chen
Collection Editors

Manuscript Submission Information

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Keywords

  • Wearable sensing
  • Unobtrusive sensing 
  • Image monitoring
  • Microfluidic
  • Flexible and tattoo sensors and electronics
  • Sensor miniaturization
  • Sensor signal processing
  • Internet of wearable things 
  • Microscale power/storage system

Published Papers (4 papers)

2019

Open AccessArticle ECG Noise Cancellation Based on Grey Spectral Noise Estimation
Sensors 2019, 19(4), 798; https://doi.org/10.3390/s19040798
Received: 13 January 2019 / Revised: 4 February 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
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Abstract
In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise [...] Read more.
In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets. Full article
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Open AccessArticle Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter
Sensors 2019, 19(3), 570; https://doi.org/10.3390/s19030570
Received: 6 December 2018 / Revised: 24 January 2019 / Accepted: 26 January 2019 / Published: 29 January 2019
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Abstract
In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a [...] Read more.
In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions. Full article
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Open AccessArticle Mobile User Indoor-Outdoor Detection through Physical Daily Activities
Sensors 2019, 19(3), 511; https://doi.org/10.3390/s19030511
Received: 11 December 2018 / Revised: 22 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
PDF Full-text (3489 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and [...] Read more.
An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications. Full article
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Open AccessArticle The Reliability and Validity of the Loadsol® under Various Walking and Running Conditions
Sensors 2019, 19(2), 265; https://doi.org/10.3390/s19020265
Received: 13 December 2018 / Revised: 3 January 2019 / Accepted: 5 January 2019 / Published: 11 January 2019
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Abstract
The assessment of loading during walking and running has historically been limited to data collection in laboratory settings or with devices that require a computer connection. This study aims to determine if the loadsol®—a single sensor wireless insole—is a valid and [...] Read more.
The assessment of loading during walking and running has historically been limited to data collection in laboratory settings or with devices that require a computer connection. This study aims to determine if the loadsol®—a single sensor wireless insole—is a valid and reliable method of assessing force. Thirty (17 male and 13 female) recreationally active individuals were recruited for a two visit study where they walked (1.3 m/s) and ran (3.0 and 3.5 m/s) at a 0%, 10% incline, and 10% decline, with the visits approximately one week apart. Ground reaction force data was collected on an instrumented treadmill (1440 Hz) and with the loadsol® (100 Hz). Ten individuals completed the day 1 protocol with a newer 200 Hz loadsol®. Intraclass correlation coefficients (ICC3,k) were used to assess validity and reliability and Bland–Altman plots were generated to better understand loadsol® validity. Across conditions, the peak force ICCs ranged from 0.78 to 0.97, which increased to 0.84–0.99 with the 200 Hz insoles. Similarly, the loading rate ICCs improved from 0.61 to 0.97 to 0.80–0.96 and impulse improved from 0.61 to 0.97 to 0.90–0.97. The 200 Hz insoles may be needed for loading rate and impulse in running. For both walking and running, the loadsol® has excellent between-day reliability (>0.76). Full article
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