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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 29884

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Special Issue Editor

Dr. Jaehong Lee

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Guest Editor

Soft Biomedical Devices Lab, Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea
Interests: stretchable electronics; fiber electronics; wearable sensors; implantable electronics; biomedical engineering; bioelectronics; biosensors

Special Issue Information

Dear Colleagues,

Wearable electronics, where electronic components or systems are integrated into wearable objects or directly used on the body, has been intensively explored for the last few decades. In particular, wearable sensors is one of the main parts of wearable electronics based on a variety of applications such as healthcare, activity tracking, sport, virtual reality, and environmental monitoring.

The present Special Issue reports recent advances in the multidisciplinary research of wearable sensors, including textile-based sensors, electronic skin, fiber electronic sensors, and various forms of wearable sensors. We look forward to and welcome your participation in this Special Issue.

Dr. Jaehong Lee
Guest Editor

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Keywords

Wearable sensors;
Textile-based sensors;
Electronic skin;
Fiber electronic sensors;
Stretchable electronic sensors;
Wearable healthcare sensing;
Flexible wearable sensors.

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Published Papers (7 papers)

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21 pages, 11387 KiB

Open AccessArticle

Estimation of the Closest In-Path Vehicle by Low-Channel LiDAR and Camera Sensor Fusion for Autonomous Vehicles

by Hyunjin Bae, Gu Lee, Jaeseung Yang, Gwanjun Shin, Gyeungho Choi and Yongseob Lim

Sensors 2021, 21(9), 3124; https://doi.org/10.3390/s21093124 - 30 Apr 2021

Cited by 8 | Viewed by 4536

Abstract

In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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15 pages, 9560 KiB

Open AccessArticle

Application and Reliability of Accelerometer-Based Arm Use Intensities in the Free-Living Environment for Manual Wheelchair Users and Able-Bodied Individuals

by Brianna M. Goodwin, Omid Jahanian, Meegan G. Van Straaten, Emma Fortune, Stefan I. Madansingh, Beth A. Cloud-Biebl, Kristin D. Zhao and Melissa M. Morrow

Sensors 2021, 21(4), 1236; https://doi.org/10.3390/s21041236 - 10 Feb 2021

Cited by 9 | Viewed by 3015

Abstract

Arm use in manual wheelchair (MWC) users is characterized by a combination of overuse and a sedentary lifestyle. This study aimed to describe the percentage of daily time MWC users and able-bodied individuals spend in each arm use intensity level utilizing accelerometers. Arm use intensity levels of the upper arms were defined as stationary, low, mid, and high from the signal magnitude area (SMA) of the segment accelerations based on in-lab MWC activities performed by eight MWC users. Accelerometry data were collected in the free-living environments from forty MWC users and 40 sex- and age-matched able-bodied individuals. The SMA intensity levels were applied to the free-living data and the percentage of time spent in each level was calculated. The SMA intensity levels were defined as, stationary: ≤0.67 g, low: 0.671–3.27 g, mid: 3.27–5.87 g, and high: >5.871 g. The dominant arm of both MWC users and able-bodied individuals was stationary for most of the day and less than one percent of the day was spent in high intensity arm activities. Increased MWC user age correlated with increased stationary arm time (R = 0.368, p = 0.019). Five and eight days of data are needed from MWC users and able-bodied individuals, respectively, to achieve reliable representation of their daily arm use intensities. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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18 pages, 28732 KiB

Open AccessArticle

A Bluetooth-Low-Energy-Based Detection and Warning System for Vulnerable Road Users in the Blind Spot of Vehicles

by Nick De Raeve, Matthias de Schepper, Jo Verhaevert, Patrick Van Torre and Hendrik Rogier

Sensors 2020, 20(9), 2727; https://doi.org/10.3390/s20092727 - 11 May 2020

Cited by 12 | Viewed by 5833

Abstract

Blind spot road accidents are a frequently occurring problem. Every year, several deaths are caused by this phenomenon, even though a lot of money is invested in raising awareness and in the development of prevention systems. In this paper, a blind spot detection and warning system is proposed, relying on Received Signal Strength Indicator (RSSI) measurements and Bluetooth Low Energy (BLE) wireless communication. The received RSSI samples are threshold-filtered, after which a weighted average is computed with a sliding window filter. The technique is validated by simulations and measurements. Finally, the strength of the proposed system is demonstrated with real-life measurements. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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18 pages, 4026 KiB

Open AccessArticle

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance

by Zijun Zhou, Shuqin Yang, Zhisen Ni, Weixing Qian, Cuihong Gu and Zekun Cao

Sensors 2020, 20(5), 1530; https://doi.org/10.3390/s20051530 - 10 Mar 2020

Cited by 12 | Viewed by 4207

Abstract

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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21 pages, 6995 KiB

Open AccessArticle

Evaluation of Joint Motion Sensing Efficiency According to the Implementation Method of SWCNT-Coated Fabric Motion Sensor

by Hyun-Seung Cho, Jin-Hee Yang, Jeong-Hwan Lee and Joo-Hyeon Lee

Sensors 2020, 20(1), 284; https://doi.org/10.3390/s20010284 - 3 Jan 2020

Cited by 15 | Viewed by 3614

Abstract

The purpose of this study was to investigate the effects of the shape and attachment position of stretchable textile piezoresistive sensors coated with single-walled carbon nanotubes on their performance in measuring the joint movements of children. The requirements for fabric motion sensors suitable for children are also identified. The child subjects were instructed to wear integrated clothing with sensors of different shapes (rectangular and boat-shaped), attachment positions (at the knee and elbow joints or 4 cm below the joints). The change in voltage caused by the elongation and contraction of the fabric sensors was measured for the flexion-extension motions of the arms and legs at 60°/s (three measurements of 10 repetitions each for the 60° and 90° angles, for a total of 60 repetitions). Their reliability was verified by analyzing the agreement between the fabric motion sensors and attached acceleration sensors. The experimental results showed that the fabric motion sensor that can measure children’s arm and leg motions most effectively is the rectangular-shaped sensor attached 4 cm below the joint. In this study, we developed a textile piezoresistive sensor suitable for measuring the joint motion of children, and analyzed the shape and attachment position of the sensor on clothing suitable for motion sensing. We showed that it is possible to sense joint motions of the human body by using flexible fabric sensors integrated into clothing. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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10 pages, 3135 KiB

Open AccessArticle

Ultra-Low Power Wearable Infant Sleep Position Sensor

by Inyeol Yun, Jinpyeo Jeung, Mijung Kim, Young-Seok Kim and Yoonyoung Chung

Sensors 2020, 20(1), 61; https://doi.org/10.3390/s20010061 - 20 Dec 2019

Cited by 9 | Viewed by 4960

Abstract

Numerous wearable sensors have been developed for a variety of needs in medical/healthcare/wellness/sports applications, but there are still doubts about their usefulness due to uncomfortable fit or frequent battery charging. Because the size or capacity of battery is the major factor affecting the convenience of wearable sensors, power consumption must be reduced. We developed a method that can significantly reduce the power consumption by introducing a signal repeater and a special switch that provides power only when needed. Antenna radiation characteristics are an important factor in wireless wearable sensors, but soft material encapsulation for comfortable fit results in poor wireless performance. We improved the antenna radiation characteristics by a local encapsulation patterning. In particular, ultra-low power operation enables the use of paper battery to achieve a very thin and flexible form factor. Also, we verified the human body safety through specific absorption rate simulations. With these methods, we demonstrated a wearable infant sleep position sensor. Infants are unable to call for help in unsafe situations, and it is not easy for caregivers to observe them all the time. Our wearable sensor detects infants’ sleep positions in real time and automatically alerts the caregivers when needed. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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13 pages, 3895 KiB

Open AccessLetter

A General Grid-Less Design Method for Location and Pressure Sensors with High Precision

by Xiaobo Zhu, Xiong Cheng, Weidong Zhang, Jiale Gao, Yijie Dai and Wenhua Gu

Sensors 2020, 20(24), 7286; https://doi.org/10.3390/s20247286 - 18 Dec 2020

Cited by 8 | Viewed by 2349

Abstract

Bionic electronic skin can accurately sense and locate surface pressure, which is widely demanded in many fields. Traditional electronic skin design usually relies on grid-architecture sensor arrays, requiring complex grid and interconnection arrangements as well as high cost. Grid-less planar sensors can solve the problem by using electrodes only at the edges, but they usually require the use of mapping software such as electrical impedance tomography to achieve high precision. In this work, a design method of high-precision grid-less planar pressure sensors based on the back-propagation (BP) neural network is proposed. The measurement precision of this method is demonstrated to be over two orders of magnitude higher than that of a grid-structure sensor array with the same electrode distribution density. Moreover, this method can be used for irregularly-shaped and non-uniform sensors, which further reduces the manufacturing difficulty and increases the application flexibility. Full article

(This article belongs to the Special Issue Wearable Electronic Sensors)

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