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17 pages, 3202 KiB

Open AccessArticle

by Jyun-Jhe Chou, Ting-Wei Chang, Xin-You Liu, Tsung-Yen Wu, Yu-Kai Chen, Ying-Tuan Hsu, Chih-Wei Chen, Tsung-Te Liu and Chi-Sheng Shih

Sensors 2022, 22(9), 3491; https://doi.org/10.3390/s22093491 - 4 May 2022

Cited by 2 | Viewed by 2504

Abstract

The majority of digital sensors rely on von Neumann architecture microprocessors to process sampled data. When the sampled data require complex computation for

24 \times 7

, the processing element will a consume significant amount of energy and computation resources. Several new sensing [...] Read more.

The majority of digital sensors rely on von Neumann architecture microprocessors to process sampled data. When the sampled data require complex computation for

24 \times 7

, the processing element will a consume significant amount of energy and computation resources. Several new sensing algorithms use deep neural network algorithms and consume even more computation resources. High resource consumption prevents such systems for

24 \times 7

deployment although they can deliver impressive results. This work adopts a Computing-In-Memory (CIM) device, which integrates a storage and analog processing unit to eliminate data movement, to process sampled data. This work designs and evaluates the CIM-based sensing framework for human pose recognition. The framework consists of uncertainty-aware training, activation function design, and CIM error model collection. The evaluation results show that the framework can improve the detection accuracy of three poses classification on CIM devices using binary weights from

33.3 %

to

91.5 %

while that on ideal CIM is

92.1 %

. Although on digital systems the accuracy is

98.7 %

with binary weight and

99.5 %

with floating weight, the energy consumption of executing 1 convolution layer on a CIM device is only 30,000 to 50,000 times less than the digital sensing system. Such a design can significantly reduce power consumption and enables battery-powered always-on sensors. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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16 pages, 6639 KiB

Open AccessArticle

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere

by Yu-Chi Chang, Chia-Hsien Lin, Alexei V. Dmitriev, Mon-Chai Hsieh, Hao-Wei Hsu, Yu-Ciang Lin, Merlin M. Mendoza, Guan-Han Huang, Lung-Chih Tsai, Yung-Hui Li and Enkhtuya Tsogtbaatar

Sensors 2022, 22(7), 2758; https://doi.org/10.3390/s22072758 - 2 Apr 2022

Cited by 2 | Viewed by 2994

Abstract

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for [...] Read more.

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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25 pages, 8351 KiB

Open AccessArticle

Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation

by Wenny Ramadha Putri, Shen-Hsuan Liu, Muhammad Saqlain Aslam, Yung-Hui Li, Chin-Chen Chang and Jia-Ching Wang

Sensors 2022, 22(6), 2133; https://doi.org/10.3390/s22062133 - 9 Mar 2022

Cited by 4 | Viewed by 3648

Abstract

Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with [...] Read more.

Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the algorithm performs. In this paper, we propose a self-supervised framework utilizing the pix2pix conditional adversarial network for generating unlimited diversified iris images. Then, the generated iris images are used to train the iris segmentation network to achieve state-of-the-art performance. We also propose an algorithm to generate iris masks based on 11 tunable parameters, which can be generated randomly. Such a framework can generate an unlimited amount of photo-realistic training data for down-stream tasks. Experimental results demonstrate that the proposed framework achieved promising results in all commonly used metrics. The proposed framework can be easily generalized to any object segmentation task with a simple fine-tuning of the mask generation algorithm. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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13 pages, 4687 KiB

Open AccessArticle

Optoelectronic Angular Displacement Measurement Technology for 2-Dimensional Mirror Galvanometer

by Shao-Kang Hung, Yu-Hsin Chung, Cheng-Lung Chen and Kai-Hung Chang

Sensors 2022, 22(3), 872; https://doi.org/10.3390/s22030872 - 24 Jan 2022

Cited by 6 | Viewed by 3434

Abstract

The mirror galvanometer is a crucial component of laser cutters/engravers. Novel two-dimensional mirror galvanometers demonstrate less trajectory distortion than traditional one-dimensional ones. This article proposes an optoelectronic sensor that measures a mirror’s inclinations in two dimensions simultaneously. The measuring range, resolution, and sampling [...] Read more.

The mirror galvanometer is a crucial component of laser cutters/engravers. Novel two-dimensional mirror galvanometers demonstrate less trajectory distortion than traditional one-dimensional ones. This article proposes an optoelectronic sensor that measures a mirror’s inclinations in two dimensions simultaneously. The measuring range, resolution, and sampling rate are ±10°, 0.0265°, and 2 kHz, respectively. With the proposed sensor, a closed-loop control can be further implemented to achieve precision laser machining. Its compact size and low cost meet the requirements of miniature laser engravers, which have become popular in recent years. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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12 pages, 1546 KiB

Open AccessArticle

A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network

by Yuan-Ho Chen, Szi-Wen Chen, Pei-Jung Chang, Hsin-Tung Hua, Shinn-Yn Lin and Rou-Shayn Chen

Sensors 2022, 22(3), 796; https://doi.org/10.3390/s22030796 - 21 Jan 2022

Cited by 11 | Viewed by 4258

Abstract

The heart is one of the human body’s vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence [...] Read more.

The heart is one of the human body’s vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence (AI)-based abnormal heart beat detection with applications for early detection and timely treatment for heart diseases. A convolutional neural network (CNN) was employed to achieve a fast and accurate identification. In order to meet the requirements of the modularity and scalability of the circuit, modular and efficient processing element (PE) units and activation function modules were designed. The proposed CNN was implemented using a TSMC 0.18

μ

m CMOS technology and had an operating frequency of 60 MHz with chip area of 1.42 mm

^{2}

and maximum power dissipation of

4.4

mW. Furthermore, six types of ECG signals drawn from the MIT-BIH arrhythmia database were used for performance evaluation. Results produced by the proposed hardware showed that the discrimination rate was

96.3

% with high efficiency in calculation, suggesting that it may be suitable for wearable devices in healthcare. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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33 pages, 4389 KiB

Open AccessArticle

Evaluation of Calibration Equations by Using Regression Analysis: An Example of Chemical Analysis

by Hsuan-Yu Chen and Chiachung Chen

Sensors 2022, 22(2), 447; https://doi.org/10.3390/s22020447 - 7 Jan 2022

Cited by 9 | Viewed by 5923

Abstract

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established [...] Read more.

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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16 pages, 16035 KiB

Open AccessArticle

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

by Chin-Chun Chang, Yen-Po Wang and Shyi-Chyi Cheng

Sensors 2021, 21(22), 7625; https://doi.org/10.3390/s21227625 - 17 Nov 2021

Cited by 14 | Viewed by 4271

Abstract

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar [...] Read more.

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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8 pages, 2343 KiB

Open AccessArticle

Low-Cost Polyethylene Terephthalate Fluidic Sensor for Ultrahigh Accuracy Measurement of Liquid Concentration Variation

by Ruey-Ching Twu, Kai-Hsuan Li and Bo-Lin Lin

Sensors 2021, 21(21), 7410; https://doi.org/10.3390/s21217410 - 8 Nov 2021

Cited by 1 | Viewed by 2288

Abstract

A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was [...] Read more.

A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was placed in a common path polarization interferometer by utilizing a heterodyne scheme. The measured phase variation of probe light was used to obtain the information regarding the concentration change in the fluidic liquids. The sensor was experimentally tested using different concentrations of sodium chloride solution showing a sensitivity of 3.52 ×10⁴ deg./refractive index unit (RIU) and a detection resolution of 6.25 × 10⁻⁶ RIU. The estimated sensitivity and detection resolutions were 5.62 × 10⁴ (deg./RIU) and 6.94 × 10⁻⁶ RIU, respectively, for the hydrochloric acid. The relationship between the measured phase and the concentration is linear with an R-squared value reaching above 0.995. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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16 pages, 5168 KiB

Open AccessArticle

The Device–Object Pairing Problem: Matching IoT Devices with Video Objects in a Multi-Camera Environment

by Kit-Lun Tong, Kun-Ru Wu and Yu-Chee Tseng

Sensors 2021, 21(16), 5518; https://doi.org/10.3390/s21165518 - 17 Aug 2021

Cited by 5 | Viewed by 3450

Abstract

IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual “objects”, these [...] Read more.

IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual “objects”, these things which are equipped with “sensing devices” also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses’ burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device–object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices’ data and demonstrating the ability to recover the missing bounding box. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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16 pages, 5029 KiB

Open AccessArticle

Slow Breathing Exercise with Multimodal Virtual Reality: A Feasibility Study

by Kun-Chan Lan, Che-Wei Li and Yushing Cheung

Sensors 2021, 21(16), 5462; https://doi.org/10.3390/s21165462 - 13 Aug 2021

Cited by 11 | Viewed by 4868

Abstract

Many studies have shown that slow breathing training is beneficial for human health. However, several factors might discourage beginners from continuing their training. For example, a long training period is generally required for benefit realization, and there is no real-time feedback to trainees [...] Read more.

Many studies have shown that slow breathing training is beneficial for human health. However, several factors might discourage beginners from continuing their training. For example, a long training period is generally required for benefit realization, and there is no real-time feedback to trainees to adjust their breathing control strategy. To raise the user’s interest in breathing exercise training, a virtual reality system with multimodal biofeedback is proposed in this work. In our system, a realistic human model of the trainee is provided in virtual reality (VR). At the same time, abdominal movements are sensed, and the breathing rate can be visualized. Being aware of the breathing rate, the trainee can regulate his or her breathing to achieve a slower breathing rate. An additional source of tactile feedback is combined with visual feedback to provide a more immersive experience for the trainees. Finally, the user’s satisfaction with the proposed system is reported through questionnaires. Most of the users find it enjoyable to use such a system for mediation training. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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20 pages, 10824 KiB

Open AccessArticle

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection

by Feng-Cheng Lin, Huu-Huy Ngo, Chyi-Ren Dow, Ka-Hou Lam and Hung Linh Le

Sensors 2021, 21(16), 5314; https://doi.org/10.3390/s21165314 - 6 Aug 2021

Cited by 77 | Viewed by 10033

Abstract

Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper proposes a student behavior recognition system based on skeleton pose [...] Read more.

Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper proposes a student behavior recognition system based on skeleton pose estimation and person detection. First, consecutive frames captured with a classroom camera were used as the input images of the proposed system. Then, skeleton data were collected using the OpenPose framework. An error correction scheme was proposed based on the pose estimation and person detection techniques to decrease incorrect connections in the skeleton data. The preprocessed skeleton data were subsequently used to eliminate several joints that had a weak effect on behavior classification. Second, feature extraction was performed to generate feature vectors that represent human postures. The adopted features included normalized joint locations, joint distances, and bone angles. Finally, behavior classification was conducted to recognize student behaviors. A deep neural network was constructed to classify actions, and the proposed system was able to identify the number of students in a classroom. Moreover, a system prototype was implemented to verify the feasibility of the proposed system. The experimental results indicated that the proposed scheme outperformed the skeleton-based scheme in complex situations. The proposed system had a 15.15% higher average precision and 12.15% higher average recall than the skeleton-based scheme did. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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11 pages, 12939 KiB

Open AccessArticle

AMBtalk: A Cardiovascular IoT Device for Ambulance Applications

by Wen-Liang Chen, Yi-Bing Lin, Ted C.-Y. Chang and Yan-Ren Lin

Sensors 2021, 21(8), 2781; https://doi.org/10.3390/s21082781 - 15 Apr 2021

Cited by 5 | Viewed by 3816

Abstract

Acute Coronary Syndrome (ACS) and other heart emergency events require immediate chest pain identification in the ambulance. Specifically, early identification and triage is required so that patients with chest pain can be quickly sent to a hospital with appropriate care facilities for treatment. [...] Read more.

Acute Coronary Syndrome (ACS) and other heart emergency events require immediate chest pain identification in the ambulance. Specifically, early identification and triage is required so that patients with chest pain can be quickly sent to a hospital with appropriate care facilities for treatment. In the traditional approach, ambulance personnel often use symptom checklists to examine the patient and make a quick decision for the target hospital. However, not every hospital has specialist facilities to handle such emergency cases. If the result of the subsequent cardiac enzyme test performed at the target hospital strongly suggests the occurrence of myocardial infarction, the patient may need to be sent to another hospital with specialist facilities, such as Percutaneous Coronary Intervention. The standard procedure is time consuming, which may result in delayed treatment and reduce patent survival rate. To resolve this issue, we propose AMBtalk (Ambulance Talk) for accurate, early ACS identification in an ambulance. AMBtalk provides real-time connection to hospital resources, which reduces the elapsed time for treatment, and therefore, improves the patient survival rate. The key to success for AMBtalk is the development of the AllCheck^® Internet of Things (IoT) device, which can accurately and quickly provide cardiovascular parameter values for early ACS identification. The interactions between the AllCheck^® IoT device, the emergency medical service center, the ambulance personnel and the hospital are achieved through the AMBtalk IoT server in the cloud network. AllCheck^® outperforms the existing cardiovascular IoT device solutions for ambulance applications. The testing results of the AllCheck^® device show 99% correlation with the results of the hospital reports. Due to its excellent performance in quick ACS identification, the AllCheck^® device was awarded the 17th Taiwan Innovators Award in 2020. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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14 pages, 5157 KiB

Open AccessArticle

Comparing Kriging Estimators Using Weather Station Data and Local Greenhouse Sensors

by Pei-Fen Kuo, Tzu-En Huang and I Gede Brawiswa Putra

Sensors 2021, 21(5), 1853; https://doi.org/10.3390/s21051853 - 6 Mar 2021

Cited by 17 | Viewed by 3637

Abstract

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting [...] Read more.

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting data and observing weather conditions. Although sensors are capable of collecting accurate weather information on-site, they can be costly and time-consuming to install and maintain. An alternative is to use the online weather stations, which are usually government-owned and free to the public; however, their accuracy is questionable because they are frequently located far from the farmers’ greenhouses. Therefore, we compared the accuracy of kriging estimators using the weather station data (collected by the Central Weather Bureau) to local sensors located in the greenhouse. The spatio-temporal kriging method was used to interpolate temperature data. The real value at the central point of the greenhouse was used for comparison. According to our results, the accuracy of the weather station estimator was slightly lower than that of the local sensor estimator. Farmers can obtain accurate estimators of environmental data by using on-site sensors; however, if they are unavailable, using a nearby weather station estimator is also acceptable. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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14 pages, 993 KiB

Open AccessArticle

Security Privacy and Policy for Cryptographic Based Electronic Medical Information System

by Hsuan-Yu Chen, Zhen-Yu Wu, Tzer-Long Chen, Yao-Min Huang and Chia-Hui Liu

Sensors 2021, 21(3), 713; https://doi.org/10.3390/s21030713 - 21 Jan 2021

Cited by 18 | Viewed by 6348

Abstract

With the development of the internet, applications have become complicated, and the relevant technology has diversified. Compared with medical applications, the significance of information technology has been expanding to include clinical auxiliary functions of medical information. This includes electronic medical records, electronic prescriptions, [...] Read more.

With the development of the internet, applications have become complicated, and the relevant technology has diversified. Compared with medical applications, the significance of information technology has been expanding to include clinical auxiliary functions of medical information. This includes electronic medical records, electronic prescriptions, medical information systems, etc. Although research on the data processing structure and format of various related systems is becoming mature, the integration is insufficient. An integrated medical information system with security policy and privacy protection, which combines e-patient records, e-prescriptions, modified smart cards, and fingerprint identification systems, and applies proxy signature and group signature, is proposed in this study. This system effectively applies and saves medical resources—satisfying the mobility of medical records, presenting the function, and security of medicine collection, and avoiding medical conflicts and profiteering to further acquire the maximum effectiveness with the least resources. In this way, this medical information system may be developed into a comprehensive function that eliminates the transmission of manual documents and maintains the safety of patient medical information. It can improve the quality of medical care and indispensable infrastructure for medical management. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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14 pages, 7440 KiB

Open AccessArticle

Traditional Chinese Medicine Pulse Diagnosis on a Smartphone Using Skin Impedance at Acupoints: A Feasibility Study

by Kun-Chan Lan, Gerhard Litscher and Te-Hsuan Hung

Sensors 2020, 20(16), 4618; https://doi.org/10.3390/s20164618 - 17 Aug 2020

Cited by 17 | Viewed by 13411

Abstract

In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosis. A pulse can be felt by applying firm fingertip pressure to the skin where the arteries travel. The pulse diagnosis has become an important tool not only [...] Read more.

In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosis. A pulse can be felt by applying firm fingertip pressure to the skin where the arteries travel. The pulse diagnosis has become an important tool not only for TCM practitioners but also for several areas of Western medicine. Many pulse measuring devices have been proposed to obtain objective pulse conditions. In the past, pulse diagnosis instruments were single-point sensing methods, which missed a lot of information. Later, multi-point sensing instruments were developed that resolved this issue but were much higher in cost and lacked mobility. In this article, based on the concept of sensor fusion, we describe a portable low-cost system for TCM pulse-type estimation using a smartphone connected to two sensors, including one photoplethysmography (PPG) sensor and one galvanic skin response (GSR) sensor. As a proof of concept, we collected five-minute PPG pulse information and skin impedance on 24 acupoints from 80 subjects. Based on these collected data, we implemented a fully connected neural network (FCN), which was able to provide high prediction accuracy (>90%) for patients with a TCM wiry pulse. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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20 pages, 70094 KiB

Open AccessCase Report

SpecTalk: Conforming IoT Implementations to Sensor Specifications

by Yi-Bing Lin and Sheng-Lin Chou

Sensors 2021, 21(16), 5260; https://doi.org/10.3390/s21165260 - 4 Aug 2021

Cited by 3 | Viewed by 3116

Abstract

Due to the fast evolution of Sensor and Internet of Things (IoT) technologies, several large-scale smart city applications have been commercially developed in recent years. In these developments, the contracts are often disputed in the acceptance due to the fact that the contract [...] Read more.

Due to the fast evolution of Sensor and Internet of Things (IoT) technologies, several large-scale smart city applications have been commercially developed in recent years. In these developments, the contracts are often disputed in the acceptance due to the fact that the contract specification is not clear, resulting in a great deal of discussion of the gray area. Such disputes often occur in the acceptance processes of smart buildings, mainly because most intelligent building systems are expensive and the operations of the sub-systems are very complex. This paper proposes SpecTalk, a platform that automatically generates the code to conform IoT applications to the Taiwan Association of Information and Communication Standards (TAICS) specifications. SpecTalk generates a program to accommodate the application programming interface of the IoT devices under test (DUTs). Then, the devices can be tested by SpecTalk following the TAICS data formats. We describe three types of tests: self-test, mutual-test, and visual test. A self-test involves the sensors and the actuators of the same DUT. A mutual-test involves the sensors and the actuators of different DUTs. A visual-test uses a monitoring camera to investigate the actuators of multiple DUTs. We conducted these types of tests in commercially deployed applications of smart campus constructions. Our experiments in the tests proved that SpecTalk is feasible and can effectively conform IoT implementations to TACIS specifications. We also propose a simple analytic model to select the frequency of the control signals for the input patterns in a SpecTalk test. Our study indicates that it is appropriate to select the control signal frequency, such that the inter-arrival time between two control signals is larger than 10 times the activation delay of the DUT. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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12 pages, 2628 KiB

Open AccessLetter

Combination of Aptamer Amplifier and Antigen-Binding Fragment Probe as a Novel Strategy to Improve Detection Limit of Silicon Nanowire Field-Effect Transistor Immunosensors

by Cao-An Vu, Pin-Hsien Pan, Yuh-Shyong Yang, Hardy Wai-Hong Chan, Yoichi Kumada and Wen-Yih Chen

Sensors 2021, 21(2), 650; https://doi.org/10.3390/s21020650 - 19 Jan 2021

Cited by 5 | Viewed by 3812

Abstract

Detecting proteins at low concentrations in high-ionic-strength conditions by silicon nanowire field-effect transistors (SiNWFETs) is severely hindered due to the weakened signal, primarily caused by screening effects. In this study, aptamer as a signal amplifier, which has already been reported by our group, [...] Read more.

Detecting proteins at low concentrations in high-ionic-strength conditions by silicon nanowire field-effect transistors (SiNWFETs) is severely hindered due to the weakened signal, primarily caused by screening effects. In this study, aptamer as a signal amplifier, which has already been reported by our group, is integrated into SiNWFET immunosensors employing antigen-binding fragments (Fab) as the receptors to improve its detection limit for the first time. The Fab-SiNWFET immunosensors were developed by immobilizing Fab onto Si surfaces modified with either 3-aminopropyltriethoxysilane (APTES) and glutaraldehyde (GA) (Fab/APTES-SiNWFETs), or mixed self-assembled monolayers (mSAMs) of polyethylene glycol (PEG) and GA (Fab/PEG-SiNWFETs), to detect the rabbit IgG at different concentrations in a high-ionic-strength environment (150 mM Bis-Tris Propane) followed by incubation with R18, an aptamer which can specifically target rabbit IgG, for signal enhancement. Empirical results revealed that the signal produced by the sensors with Fab probes was greatly enhanced compared to the ones with whole antibody (Wab) after detecting similar concentrations of rabbit IgG. The Fab/PEG-SiNWFET immunosensors exhibited an especially improved limit of detection to determine the IgG level down to 1 pg/mL, which has not been achieved by the Wab/PEG-SiNWFET immunosensors. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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18 pages, 4785 KiB

Open AccessLetter

Improved Diver Communication System by Combining Optical and Electromagnetic Trackers

by Aman Kataria, Smarajit Ghosh, Vinod Karar, Takshi Gupta, Kathiravan Srinivasan and Yuh-Chung Hu

Sensors 2020, 20(18), 5084; https://doi.org/10.3390/s20185084 - 7 Sep 2020

Cited by 9 | Viewed by 3692

Abstract

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also [...] Read more.

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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