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Special Issue "Wearable Smart Devices"

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

Deadline for manuscript submissions: closed (31 May 2018)

Special Issue Editors

Guest Editor
Dr. Geoff Merrett

Centre for IoT and Pervasive Systems, University of Southampton, Southampton SO17 1BJ, UK
Website | E-Mail
Phone: +44 (0)23 8059 2775
Interests: mobile and embedded systems; power/energy management; energy harvesting; energy-driven computing; intermittent computing
Guest Editor
Dr. Russel Torah

Smart Electronic Materials and Systems Research Group, Department of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK
Website | E-Mail
Interests: smart textiles; printed electronics; wearable electronics; electronics inks; medical textiles; energy harvesting

Special Issue Information

Dear Colleagues,

Over the past decade, we have seen considerable developments in smart wearable devices and technology. The proliferation of devices—from fitness trackers and healthcare monitors to smart watches and mobile computing—has been fuelled by a combination of advances in underpinning technology and consumer demand/acceptance. However, despite its growing maturity, there remain numerous challenges in a broad range of areas that require research effort in order to further the technology.

To highlight some of the latest developments in this exciting and relevant field, we invite you to consider submitting a manuscript to our upcoming Special Issue “Wearable Smart Devices”. Both research papers and review articles will be considered. We welcome submissions spanning topics across sensor devices, wearable technologies, and embedded intelligence for smart wearable devices.

Dr. Geoff Merrett
Dr. Russel Torah
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 papers will be 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 monthly 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 1800 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

In the context of smart wearable devices, we solicit papers covering (but not limited to) one or more of the following topics:

  • Sensor devices and technologies
  • Healthcare and medical prototypes and applications
  • Innovative applications and case studies
  • Hardware and software co-design and architectures
  • Smart textiles and printed electronics/sensors
  • Miniaturisation, integration, packaging, wearability and user-acceptance
  • Reliability, washability and durability
  • Wearable IoT
  • Intelligent algorithms, data processing and inference
  • Data fusion or processing for accurate signal estimation
  • Networking and interoperability
  • Energy harvesting
  • System energy/power management

Published Papers (15 papers)

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Research

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Open AccessArticle An Inexpensive and Easy to Use Cervical Range of Motion Measurement Solution Using Inertial Sensors
Sensors 2018, 18(8), 2582; https://doi.org/10.3390/s18082582
Received: 31 May 2018 / Revised: 27 July 2018 / Accepted: 3 August 2018 / Published: 7 August 2018
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Abstract
Neck injuries and the related pain have a high prevalence and represent an important health problem. To properly diagnose and treat them, practitioners need an accurate system for measuring Cervical Range Of Motion (CROM). This article describes the development and validation of an
[...] Read more.
Neck injuries and the related pain have a high prevalence and represent an important health problem. To properly diagnose and treat them, practitioners need an accurate system for measuring Cervical Range Of Motion (CROM). This article describes the development and validation of an inexpensive, small (4 cm × 4 cm × 8 cm), light (< 200 g) and easy to use solution for measuring CROM using wearable inertial sensors. The proposed solution has been designed with the clinical practice in mind, after consulting with practitioners. It is composed of: (a) two wearable wireless MEMS-based inertial devices, (b) a recording and report generation software application and (c) a measurement protocol for assessing CROM. The solution provides accurate (none of our results is outside the ROM ranges when compared with previously published results based on an optical tracking device) and reliable measurements (ICC = 0.93 for interrater reliability when compared with an optical tracking device and ICC > 0.90 for test-retest reliability), surpassing the popular CROM instrument’s capabilities and precision. It also fulfills the needs for clinical practice attending to effectiveness, efficiency (4 min from setup to final report) and user’s satisfaction (as reported by practitioners). The solution has been certified for mass-production and use in medical environments. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring
Sensors 2018, 18(8), 2501; https://doi.org/10.3390/s18082501
Received: 23 May 2018 / Revised: 22 July 2018 / Accepted: 22 July 2018 / Published: 1 August 2018
Cited by 1 | PDF Full-text (2406 KB) | HTML Full-text | XML Full-text
Abstract
Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation,
[...] Read more.
Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Development of User-Friendly Wearable Electronic Textiles for Healthcare Applications
Sensors 2018, 18(8), 2410; https://doi.org/10.3390/s18082410
Received: 28 June 2018 / Revised: 18 July 2018 / Accepted: 20 July 2018 / Published: 25 July 2018
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Abstract
This paper presents research into a user-friendly electronic sleeve (e-sleeve) with integrated electrodes in an array for wearable healthcare. The electrode array was directly printed onto an everyday clothing fabric using screen printing. The fabric properties and designed structures of the e-sleeve were
[...] Read more.
This paper presents research into a user-friendly electronic sleeve (e-sleeve) with integrated electrodes in an array for wearable healthcare. The electrode array was directly printed onto an everyday clothing fabric using screen printing. The fabric properties and designed structures of the e-sleeve were assessed and refined through interaction with end users. Different electrode array layouts were fabricated to optimize the user experience in terms of comfort, effectivity and ease of use. The e-sleeve uses dry electrodes to facilitate ease of use and the electrode array can survive bending a sufficient number of times to ensure an acceptable usage lifetime. Different cleaning methods (washing and wiping) have been identified to enable reuse of the e-sleeve after contamination during use. The application of the e-sleeve has been demonstrated via muscle stimulation on the upper limb to achieve functional tasks (e.g., hand opening, pointing) for eight stroke survivors. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle A Novel Method for Estimating Free Space 3D Point-of-Regard Using Pupillary Reflex and Line-of-Sight Convergence Points
Sensors 2018, 18(7), 2292; https://doi.org/10.3390/s18072292
Received: 13 June 2018 / Revised: 11 July 2018 / Accepted: 13 July 2018 / Published: 15 July 2018
Cited by 1 | PDF Full-text (6227 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a novel 3D gaze estimation method for a wearable gaze tracking device is proposed. This method is based on the pupillary accommodation reflex of human vision. Firstly, a 3D gaze measurement model is built. By uniting the line-of-sight convergence point
[...] Read more.
In this paper, a novel 3D gaze estimation method for a wearable gaze tracking device is proposed. This method is based on the pupillary accommodation reflex of human vision. Firstly, a 3D gaze measurement model is built. By uniting the line-of-sight convergence point and the size of the pupil, this model can be used to measure the 3D Point-of-Regard in free space. Secondly, a gaze tracking device is described. By using four cameras and semi-transparent mirrors, the gaze tracking device can accurately extract the spatial coordinates of the pupil and eye corner of the human eye from images. Thirdly, a simple calibration process of the measuring system is proposed. This method can be sketched as follows: (1) each eye is imaged by a pair of binocular stereo cameras, and the setting of semi-transparent mirrors can support a better field of view; (2) the spatial coordinates of the pupil center and the inner corner of the eye in the images of the stereo cameras are extracted, and the pupil size is calculated with the features of the gaze estimation method; (3) the pupil size and the line-of-sight convergence point when watching the calibration target at different distances are computed, and the parameters of the gaze estimation model are determined. Fourthly, an algorithm for searching the line-of-sight convergence point is proposed, and the 3D Point-of-Regard is estimated by using the obtained line-of-sight measurement model. Three groups of experiments were conducted to prove the effectiveness of the proposed method. This approach enables people to obtain the spatial coordinates of the Point-of-Regard in free space, which has great potential in the application of wearable devices. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Mouthwitch: A Novel Head Mount Type Hands-Free Input Device that Uses the Movement of the Temple to Control a Camera
Sensors 2018, 18(7), 2273; https://doi.org/10.3390/s18072273
Received: 30 May 2018 / Revised: 11 July 2018 / Accepted: 12 July 2018 / Published: 13 July 2018
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Abstract
We have developed an interface (mouthwitch) for a head-mounted type camera with which pictures can be taken with a head-mounted camera, hands-free, simply by “opening your mouth continuously for approximately one second and then closing it again”. This mouthwitch uses a sensor equipped
[...] Read more.
We have developed an interface (mouthwitch) for a head-mounted type camera with which pictures can be taken with a head-mounted camera, hands-free, simply by “opening your mouth continuously for approximately one second and then closing it again”. This mouthwitch uses a sensor equipped with an LED and photo transistor on the temple to optically measure the changes in the form of the temple that occur when the mouth is opened and closed. Eight test subjects (males and females aged between 21 and 44 years old) performed evaluation tests using this mouthwitch when resting, speaking, chewing, walking, and running. The results showed that all test subjects were able to open and close the mouth, and the measurement results pertaining to the temple shape changes that occurred at this time were highly reproducible. Additionally, the average value for accuracy obtained for the eight test subjects through the verification tests was 100% when resting, chewing, or walking, and 99.8% when speaking or running. Similarly, the average values for precision were 100% for all items, and the average values for recall were 100% when resting or chewing, 98.8% when speaking, 97.5% when walking, and 87.5% when running. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle The Accuracy of the Detection of Body Postures and Movements Using a Physical Activity Monitor in People after a Stroke
Sensors 2018, 18(7), 2167; https://doi.org/10.3390/s18072167
Received: 31 May 2018 / Revised: 29 June 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
Cited by 1 | PDF Full-text (1654 KB) | HTML Full-text | XML Full-text
Abstract
Background: In stroke rehabilitation not only are the levels of physical activity important, but body postures and movements performed during one’s daily-life are also important. This information is provided by a new one-sensor accelerometer that is commercially available, low-cost, and user-friendly. The present
[...] Read more.
Background: In stroke rehabilitation not only are the levels of physical activity important, but body postures and movements performed during one’s daily-life are also important. This information is provided by a new one-sensor accelerometer that is commercially available, low-cost, and user-friendly. The present study examines the accuracy of this activity monitor (Activ8) in detecting several classes of body postures and movements in people after a stroke. Methods: Twenty-five people after a stroke participated in an activity protocol with either basic activities or daily-life activities performed in a laboratory and/or at home. Participants wore an Activ8 on their less-affected thigh. The primary outcome was the difference in registered time for the merged class “upright position” (standing/walking/running) between the Activ8 and the video recording (the reference method). Secondary analyses focused on classes other than “upright position”. Results: The Activ8 underestimated the merged class “upright position” by 3.8% (775 s). The secondary analyses showed an overestimation of “lying/sitting” (4.5% (569 s)) and of “cycling” (6.5% (206 s)). The differences were lowest for basic activities in the laboratory and highest for daily-life activities at home. Conclusions: The Activ8 is sufficiently accurate in detecting different classes of body postures and movements of people after a stroke during basic activities and daily-life activities in a laboratory and/or at home. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Thermal Energy Harvesting on the Bodily Surfaces of Arms and Legs through a Wearable Thermo-Electric Generator
Sensors 2018, 18(6), 1927; https://doi.org/10.3390/s18061927
Received: 19 March 2018 / Revised: 8 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator
[...] Read more.
This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 μW. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Developing an Acoustic Sensing Yarn for Health Surveillance in a Military Setting
Sensors 2018, 18(5), 1590; https://doi.org/10.3390/s18051590
Received: 23 April 2018 / Revised: 8 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
Cited by 2 | PDF Full-text (4530 KB) | HTML Full-text | XML Full-text
Abstract
Overexposure to high levels of noise can cause permanent hearing disorders, which have a significant adverse effect on the quality of life of those affected. Injury due to noise can affect people in a variety of careers including construction workers, factory workers, and
[...] Read more.
Overexposure to high levels of noise can cause permanent hearing disorders, which have a significant adverse effect on the quality of life of those affected. Injury due to noise can affect people in a variety of careers including construction workers, factory workers, and members of the armed forces. By monitoring the noise exposure of workers, overexposure can be avoided and suitable protective equipment can be provided. This work focused on the creation of a noise dosimeter suitable for use by members of the armed forces, where a discrete dosimeter was integrated into a textile helmet cover. In this way the sensing elements could be incorporated very close to the ears, providing a highly representative indication of the sound level entering the body, and also creating a device that would not interfere with military activities. This was achieved by utilising commercial microelectromechanical system microphones integrated within the fibres of yarn to create an acoustic sensing yarn. The acoustic sensing yarns were fully characterised over a range of relevant sound levels and frequencies at each stage in the yarn production process. The yarns were ultimately integrated into a knitted helmet cover to create a functional acoustic sensing helmet cover prototype. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation
Sensors 2018, 18(5), 1506; https://doi.org/10.3390/s18051506
Received: 5 April 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
Cited by 5 | PDF Full-text (12727 KB) | HTML Full-text | XML Full-text
Abstract
Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung
[...] Read more.
Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors
Sensors 2018, 18(5), 1483; https://doi.org/10.3390/s18051483
Received: 23 February 2018 / Revised: 14 April 2018 / Accepted: 21 April 2018 / Published: 9 May 2018
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Abstract
The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between
[...] Read more.
The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75–92%) was obtained in identifying P300 evoked potentials. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle A Wearable Body Controlling Device for Application of Functional Electrical Stimulation
Sensors 2018, 18(4), 1251; https://doi.org/10.3390/s18041251
Received: 5 March 2018 / Revised: 14 April 2018 / Accepted: 17 April 2018 / Published: 18 April 2018
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Abstract
In this research, we describe a new balancing device used to stabilize the rear quarters of a patient dog with spinal cord injuries. Our approach uses inertial measurement sensing and direct leg actuation to lay a foundation for eventual muscle control by means
[...] Read more.
In this research, we describe a new balancing device used to stabilize the rear quarters of a patient dog with spinal cord injuries. Our approach uses inertial measurement sensing and direct leg actuation to lay a foundation for eventual muscle control by means of direct functional electrical stimulation (FES). During this phase of development, we designed and built a mechanical test-bed to develop the control and stimulation algorithms before we use the device on our animal subjects. We designed the bionic test-bed to mimic the typical walking gait of a dog and use it to develop and test the functionality of the balancing device for stabilization of patient dogs with hindquarter paralysis. We present analysis for various muscle stimulation and balancing strategies, and our device can be used by veterinarians to tailor the stimulation strength and temporal distribution for any individual patient dog. We develop stabilizing muscle stimulation strategies using the robotic test-bed to enhance walking stability. We present experimental results using the bionic test-bed to demonstrate that the balancing device can provide an effective sensing strategy and deliver the required motion control commands for stabilizing an actual dog with a spinal cord injury. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle A Novel GMM-Based Behavioral Modeling Approach for Smartwatch-Based Driver Authentication
Sensors 2018, 18(4), 1007; https://doi.org/10.3390/s18041007
Received: 14 January 2018 / Revised: 27 February 2018 / Accepted: 1 March 2018 / Published: 28 March 2018
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Abstract
All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior.
[...] Read more.
All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created an experimental system that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication—an equal error rate (EER) of 4.62% in the simulated environment and an EER of 7.86% in the real-traffic environment—confirm the feasibility of this approach. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle A Portable Wireless Communication Platform Based on a Multi-Material Fiber Sensor for Real-Time Breath Detection
Sensors 2018, 18(4), 973; https://doi.org/10.3390/s18040973
Received: 6 February 2018 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
Cited by 1 | PDF Full-text (1211 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a
[...] Read more.
In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a spiral-shaped antenna made from a multi-material fiber connected to a compact transmitter. Based on the resonance frequency of the antenna at approximately 2.4 GHz, the breathing sensor relies on its Bluetooth transmitter. The contactless and non-invasive sensor is designed without compromising the user’s comfort. The sensing mechanism of the system is based on the detection of the signal amplitude transmitted wirelessly by the sensor, which is found to be sensitive to strain. We demonstrate the capability of the platform to detect the breathing rates of four male volunteers who are not in movement. The breathing pattern is obtained through the received signal strength indicator (RSSI) which is filtered and analyzed with home-made algorithms in the portable system. Numerical simulations of human breath are performed to support the experimental detection, and both results are in a good agreement. Slow, fast, regular, irregular, and shallow breathing types are successfully recorded within a frequency interval of 0.16–1.2 Hz, leading to a breathing rate varying from 10 to 72 breaths per minute. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality
Sensors 2018, 18(2), 416; https://doi.org/10.3390/s18020416
Received: 10 December 2017 / Revised: 23 January 2018 / Accepted: 26 January 2018 / Published: 1 February 2018
Cited by 2 | PDF Full-text (15954 KB) | HTML Full-text | XML Full-text
Abstract
Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer
[...] Read more.
Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Review

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Open AccessReview Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies
Sensors 2018, 18(8), 2414; https://doi.org/10.3390/s18082414
Received: 31 May 2018 / Revised: 19 July 2018 / Accepted: 21 July 2018 / Published: 25 July 2018
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Abstract
Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The
[...] Read more.
Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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