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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 January 2016) | Viewed by 124846

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Special Issue Editor

Prof. Dr. Oliver Amft

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Chair of Sensor Technology, ACTLab research group, University of Passau, Innstrasse 43, D-94032 Passau, Germany
Interests: wearable computing, pattern recognition, ubiquitous computing, biomedical engineering, gesture recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites innovative contributions in the quickly growing field of wearable and on-body sensors. Continuous miniaturization leads to unobtrusive wearable sensors, which are adequate for various applications, including in sports, patient care, assisted living, and others. Novel sensor modalities and sensor implementations could foster further innovative applications, improve artifact resilience and robustness, enable ultra-low power operation, and generally support the quicker adoption of wearables. Wearable sensors can be integrated into clothes or body patches, and can be found in implants and accessories. Enabling reliable sensor functions in textile fabrics, as well as ensuring skin/bio-compatibility, flexibility, and low profiles are essential. However, such necessities present technically challenging problems because of physical and material constraints, variable use patterns, and specific environmental conditions in or at the body, including mechanical strain and sweat. Further research on wearable sensor design and integration is needed to obtain sustainable technical solutions that wearers can routinely use. Wearables for animals are a new trend that further supports the expansion of wearable sensor technology beyond the human body.

Real-life evaluation is an essential tool for verifying sensor functionality, robustness, and user acceptance. Furthermore, deriving fundamental insights in wearable sensor design and performance is essential where innovative sensor simulation and sensor modeling techniques can be beneficial.

Contributions may include, but are not limited to:

Novel, wearable sensor modalities, including chemical, physical, electrical, optical, acoustic, etc. devices, and innovative miniature on-body sensor integration.
Innovative sensor simulation and modeling techniques that are especifically insightful for wearable sensor development and deployment.
Textile and body patch sensor integration in combination with real-life evaluation.
Implantable sensor design and evaluation.
Wearable sensor design and implementation (as optimized for animals).
Innovative applications of wearable sensor systems.

Prof. Dr. Oliver Amft
Guest Editor

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

body-worn sensors
wearable computing
biopotential sensors
inertial sensors
implants

Published Papers (11 papers)

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Research

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2714 KiB

Open AccessArticle

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

by Simone Ciotti, Edoardo Battaglia, Nicola Carbonaro, Antonio Bicchi, Alessandro Tognetti and Matteo Bianchi

Sensors 2016, 16(6), 811; https://doi.org/10.3390/s16060811 - 02 Jun 2016

Cited by 27 | Viewed by 9847

Abstract

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness. Full article

(This article belongs to the Special Issue Wearable Sensors)

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5224 KiB

Open AccessArticle

Improving Pulse Rate Measurements during Random Motion Using a Wearable Multichannel Reflectance Photoplethysmograph

by Kristen M. Warren, Joshua R. Harvey, Ki H. Chon and Yitzhak Mendelson

Sensors 2016, 16(3), 342; https://doi.org/10.3390/s16030342 - 07 Mar 2016

Cited by 40 | Viewed by 7338

Abstract

Photoplethysmographic (PPG) waveforms are used to acquire pulse rate (PR) measurements from pulsatile arterial blood volume. PPG waveforms are highly susceptible to motion artifacts (MA), limiting the implementation of PR measurements in mobile physiological monitoring devices. Previous studies have shown that multichannel photoplethysmograms can successfully acquire diverse signal information during simple, repetitive motion, leading to differences in motion tolerance across channels. In this paper, we investigate the performance of a custom-built multichannel forehead-mounted photoplethysmographic sensor under a variety of intense motion artifacts. We introduce an advanced multichannel template-matching algorithm that chooses the channel with the least motion artifact to calculate PR for each time instant. We show that for a wide variety of random motion, channels respond differently to motion artifacts, and the multichannel estimate outperforms single-channel estimates in terms of motion tolerance, signal quality, and PR errors. We have acquired 31 data sets consisting of PPG waveforms corrupted by random motion and show that the accuracy of PR measurements achieved was increased by up to 2.7 bpm when the multichannel-switching algorithm was compared to individual channels. The percentage of PR measurements with error ≤ 5 bpm during motion increased by 18.9% when the multichannel switching algorithm was compared to the mean PR from all channels. Moreover, our algorithm enables automatic selection of the best signal fidelity channel at each time point among the multichannel PPG data. Full article

(This article belongs to the Special Issue Wearable Sensors)

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2602 KiB

Open AccessArticle

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

by Seyed M. A. Salehizadeh, Duy Dao, Jeffrey Bolkhovsky, Chae Cho, Yitzhak Mendelson and Ki H. Chon

Sensors 2016, 16(1), 10; https://doi.org/10.3390/s16010010 - 23 Dec 2015

Cited by 122 | Viewed by 9807

Abstract

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities. Full article

(This article belongs to the Special Issue Wearable Sensors)

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979 KiB

Open AccessArticle

A Vibrotactile and Plantar Force Measurement-Based Biofeedback System: Paving the Way towards Wearable Balance-Improving Devices

by Christina Zong-Hao Ma, Anson Hong-Ping Wan, Duo Wai-Chi Wong, Yong-Ping Zheng and Winson Chiu-Chun Lee

Sensors 2015, 15(12), 31709-31722; https://doi.org/10.3390/s151229883 - 15 Dec 2015

Cited by 33 | Viewed by 7624

Abstract

Although biofeedback systems have been used to improve balance with success, they were confined to hospital training applications. Little attempt has been made to investigate the use of in-shoe plantar force measurement and wireless technology to turn hospital training biofeedback systems into wearable devices. This research developed a wearable biofeedback system which detects body sway by analyzing the plantar force and provides users with the corresponding haptic cues. The effects of this system were evaluated in thirty young and elderly subjects with simulated reduced foot sensation. Subjects performed a Romberg test under three conditions: (1) no socks, system turned-off; (2) wearing five layers of socks, system turned-off; (3) wearing five layers of socks, and system turned-on. Degree of body sway was investigated by computing the center of pressure (COP) movement measured by a floor-mounted force platform. Plantar tactile sensation was evaluated using a monofilament test. Wearing multiple socks significantly decreased the plantar tactile sensory input (p < 0.05), and increased the COP parameters (p < 0.017), indicating increased postural sway. After turning on the biofeedback system, the COP parameters decreased significantly (p < 0.017). The positive results of this study should inspire future development of wearable plantar force-based biofeedback systems for improving balance in people with sensory deficits. Full article

(This article belongs to the Special Issue Wearable Sensors)

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2740 KiB

Open AccessArticle

Detection of Site-Specific Blood Flow Variation in Humans during Running by a Wearable Laser Doppler Flowmeter

by Wataru Iwasaki, Hirofumi Nogami, Satoshi Takeuchi, Masutaka Furue, Eiji Higurashi and Renshi Sawada

Sensors 2015, 15(10), 25507-25519; https://doi.org/10.3390/s151025507 - 05 Oct 2015

Cited by 32 | Viewed by 14094

Abstract

Wearable wireless physiological sensors are helpful for monitoring and maintaining human health. Blood flow contains abundant physiological information but it is hard to measure blood flow during exercise using conventional blood flowmeters because of their size, weight, and use of optic fibers. To resolve these disadvantages, we previously developed a micro integrated laser Doppler blood flowmeter using microelectromechanical systems technology. This micro blood flowmeter is wearable and capable of stable measurement signals even during movement. Therefore, we attempted to measure skin blood flow at the forehead, fingertip, and earlobe of seven young men while running as a pilot experiment to extend the utility of the micro blood flowmeter. We measured blood flow in each subject at velocities of 6, 8, and 10 km/h. We succeeded in obtaining stable measurements of blood flow, with few motion artifacts, using the micro blood flowmeter, and the pulse wave signal and motion artifacts were clearly separated by conducting frequency analysis. Furthermore, the results showed that the extent of the changes in blood flow depended on the intensity of exercise as well as previous work with an ergometer. Thus, we demonstrated the capability of this wearable blood flow sensor for measurement during exercise. Full article

(This article belongs to the Special Issue Wearable Sensors)

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1139 KiB

Open AccessArticle

Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors

by Dae Y. Kang, Yun-Soung Kim, Gladys Ornelas, Mridu Sinha, Keerthiga Naidu and Todd P. Coleman

Sensors 2015, 15(9), 23459-23476; https://doi.org/10.3390/s150923459 - 16 Sep 2015

Cited by 35 | Viewed by 12071

Abstract

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach. Full article

(This article belongs to the Special Issue Wearable Sensors)

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1567 KiB

Open AccessArticle

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

by Gang Li and Wan-Young Chung

Sensors 2015, 15(8), 20873-20893; https://doi.org/10.3390/s150820873 - 21 Aug 2015

Cited by 77 | Viewed by 11330

Abstract

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness. Full article

(This article belongs to the Special Issue Wearable Sensors)

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3931 KiB

Open AccessArticle

Daily Quantity of Infant Leg Movement: Wearable Sensor Algorithm and Relationship to Walking Onset

by Beth A. Smith, Ivan A. Trujillo-Priego, Christianne J. Lane, James M. Finley and Fay B. Horak

Sensors 2015, 15(8), 19006-19020; https://doi.org/10.3390/s150819006 - 04 Aug 2015

Cited by 52 | Viewed by 7427

Abstract

Background: Normative values are lacking for daily quantity of infant leg movements. This is critical for understanding the relationship between the quantity of leg movements and onset of independent walking, and will begin to inform early therapy intervention for infants at risk for developmental delay. Methods: We used wearable inertial movement sensors to record full-day leg movement activity from 12 infants with typical development, ages 1–12 months. Each infant was tested three times across 5 months, and followed until the onset of independent walking. We developed and validated an algorithm to identify infant-produced leg movements. Results: Infants moved their legs tens of thousands of times per day. There was a significant effect of leg movement quantity on walking onset. Infants who moved their legs more walked later than infants who moved their legs less, even when adjusting for age, developmental level or percentile length. We will need a much larger sample to adequately capture and describe the effect of movement experience on developmental rate. Our algorithm defines a leg movement in a specific way (each pause or change in direction is counted as a new movement), and further assessment of movement characteristics are necessary before we can fully understand and interpret our finding that infants who moved their legs more walked later than infants who moved their legs less. Conclusions: We have shown that typically-developing infants produce thousands of leg movements in a typical day, and that this can be accurately captured in the home environment using wearable sensors. In our small sample we can identify there is an effect of leg movement quantity on walking onset, however we cannot fully explain it. Full article

(This article belongs to the Special Issue Wearable Sensors)

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Review

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236 KiB

Open AccessReview

Gait Partitioning Methods: A Systematic Review

by Juri Taborri, Eduardo Palermo, Stefano Rossi and Paolo Cappa

Sensors 2016, 16(1), 66; https://doi.org/10.3390/s16010066 - 06 Jan 2016

Cited by 260 | Viewed by 13191

Abstract

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments. Full article

(This article belongs to the Special Issue Wearable Sensors)

1548 KiB

Open AccessReview

Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

by Qi Wei Oung, Hariharan Muthusamy, Hoi Leong Lee, Shafriza Nisha Basah, Sazali Yaacob, Mohamed Sarillee and Chia Hau Lee

Sensors 2015, 15(9), 21710-21745; https://doi.org/10.3390/s150921710 - 31 Aug 2015

Cited by 57 | Viewed by 11631

Abstract

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD. Full article

(This article belongs to the Special Issue Wearable Sensors)

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2570 KiB

Open AccessReview

Wearable Sensor Systems for Infants

by Zhihua Zhu, Tao Liu, Guangyi Li, Tong Li and Yoshio Inoue

Sensors 2015, 15(2), 3721-3749; https://doi.org/10.3390/s150203721 - 05 Feb 2015

Cited by 139 | Viewed by 19510

Abstract

Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant’s body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future. Full article

(This article belongs to the Special Issue Wearable Sensors)

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