論文 - 大槻　知明

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Chen R., Li L., Ma Y., Gong Y., Guo Y., Ohtsuki T., Pan M.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17403 - 17416 2022年09月

　概要を見る

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants' location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries' prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

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Wi-Fi-Based Fall Detection Using Spectrogram Image of Channel State Information

Nakamura T., Bouazizi M., Yamamoto K., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17220 - 17234 2022年09月

　概要を見る

Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace limited. However, in the conventional work on Wi-Fi CSI-based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the signal-to-noise-power ratio (SNR) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this article, we propose a spectrogram-image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding-time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

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Sub-GHz Two-Way Ranging Based on Phase Detection for Remote Keyless Entry Systems

Seto I., Otaka S., Yoshida H., Nonin K., Nishikawa M., Kato T., Nito Y., Ishiwata H., Otsuki T.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 9 ） 9705 - 9720 2022年09月

ISSN  00189545

　概要を見る

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle.

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Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection

Yang S., Bouazizi M., Cao Y., Ohtsuki T.

Sensors （Sensors）  22 （ 16 ）  2022年08月

ISSN  14248220

　概要を見る

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

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2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Bouazizi M., Ye C., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 13 ） 10872 - 10890 2022年07月

　概要を見る

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

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A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Zhao R., Gui G., Xue Z., Yin J., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 12 ） 9960 - 9972 2022年06月

　概要を見る

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

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Fully Convolutional Neural Network-Based CSI Limited Feedback for FDD Massive MIMO Systems

Fan G., Sun J., Gui G., Gacanin H., Adebisi B., Ohtsuki T.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 2 ） 672 - 682 2022年06月

　概要を見る

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state information (CSI) directly from its reciprocal uplink CSI. Hence, the estimated downlink CSI needs to be continuously fed back to the base station (BS) from the user equipment (UE), consuming valuable bandwidth resources. This is exacerbated, in massive MIMO, with the increase of the antennas at the BS. This paper propose a fully convolutional neural network (FullyConv) to compress and decompress the downlink CSI. FullyConv will improve the reconstruction accuracy of downlink CSI and reduce the training parameters and computational resources. Besides, we add a quantization module in the encoder and a dequantization module in the decoder of the FullyConv to simulate a real feedback scenario. Experimental results demonstrate that the proposed FullyConv is better than the baseline on reconstruction performance and reduction of the storage and computational overhead. Furthermore, the FullyConv added quantization and dequantization modules is robust to quantization error in real feedback scenarios.

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An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

Arumugasamy M.K., Bouazizi M., Ohtsuki T.

Sensors （Sensors）  22 （ 10 ）  2022年05月

ISSN  14248220

　概要を見る

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

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An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

Zhao H., Tang J., Adebisi B., Ohtsuki T., Gui G., Zhu H.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 3 ） 2939 - 2948 2022年03月

ISSN  00189545

　概要を見る

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement.

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Lightweight Automatic Modulation Classification Based on Decentralized Learning

Fu X., Gui G., Wang Y., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 1 ） 57 - 70 2022年03月

　概要を見る

Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model consolidation and lightweight design. Specifically, the model consolidation is realized by a central device (CD) for edge device (ED) model averaging (MA) and multiple EDs for ED model training. The lightweight is designed by separable convolutional neural network (S-CNN), in which the separable convolutional layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolutional neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about N times of it, where N is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset D1 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} and about 5 times that of S-CNN-based CentAMC in dataset D2 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM}), while the communication overhead is reduced more than 35%.

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Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

Bouazizi M., Ye C., Ohtsuki T.

Information (Switzerland) （Information (Switzerland)）  13 （ 3 ）  2022年03月

　概要を見る

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

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Blockchain-Based Task Offloading for Edge Computing on Low-Quality Data via Distributed Learning in the Internet of Energy

Liu Y., Guan X., Peng Y., Chen H., Ohtsuki T., Han Z.

IEEE Journal on Selected Areas in Communications （IEEE Journal on Selected Areas in Communications）  40 （ 2 ） 657 - 676 2022年02月

ISSN  07338716

　概要を見る

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

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Dementia Detection Using Language Models and Transfer Learning

Bouazizi M., Zheng C., Ohtsuki T.

ACM International Conference Proceeding Series （ACM International Conference Proceeding Series）     152 - 157 2022年01月

　概要を見る

Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.

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A Multi-Layer Hybrid Network With Its Application in Fetal Heart Rate Monitoring

Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

IEEE Signal Processing Letters （IEEE Signal Processing Letters）  29   1207 - 1211 2022年

ISSN  10709908

　概要を見る

Fetal heart rate monitoring is an enormous challenge since the observed fetal electrocardiography (ECG) signal is typically characterized by a very low signal-to-noise ratio (SNR). In this letter, we aim to improve the accuracy of heartbeat detection by proposing an adaptive template for removing the maternal cycle. The template is formed by a matrix, each row of which consists of an abdominal recorded signal (ADS). It can be updated by integrating the incoming cycle while removing the contribution of the previous recording. This process is conducted by considering a discriminator to adapt the non-stationarity of each incoming cycle. Furthermore, to suppress the morphological change caused by noise, we propose a novel multi-layer hybrid network to reconstruct the chest maternal ECG (chest mECG) morphology from a set of templates. The approach has a deep structure of each layer consisting of a reservoir layer and an encoder layer. The reservoir layer explores multi-scale dynamics by transforming the input series into a high-dimensional space. The encoder layer achieves the collection of the encoder features from the output of the reservoir layer. Once the model is built, the output weight of a direct connection is trained by solving a regression problem. Experimental results show that the proposed method has a better performance compared with some classical approaches.

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Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access （IEEE Access）  10   68085 - 68098 2022年

　概要を見る

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

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Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis

Chen Y., Bouazizi M., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     6433 - 6438 2022年

ISSN  23340983

　概要を見る

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.

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Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Shi S., Wang Y., Dong H., Gui G., Ohtsuki T.

INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops （INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops）  2022年

　概要を見る

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.

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Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   55958 - 55987 2022年

　概要を見る

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, we run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.

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Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   1296 - 1299 2022年

ISSN  1557170X

　概要を見る

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

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Non-contact Blood Pressure Estimation Method Based on Blood Pressure Category Classification

Ishizaka S., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2676 - 2679 2022年

ISSN  1557170X

　概要を見る

In recent years, non-contact Blood Pressure (BP) measurement has been attracting attention for measuring our health status in daily life. A Doppler radar can observe pulse waves caused by chest wall displacement due to heartbeat. BP can be estimated by constructing a BP estimation model using BP-related features obtained from the pulse wave. However, compared to when modeling for each subject, the BP esti-mation accuracy deteriorates significantly when modeling with multiple subjects including the testing subject. To deal with this limitation, BP category classification has been introduced into PhotoPlethysmoGraphy (PPG)-based BP estimation. In this paper, we develop a Doppler radar-based BP estimation method based on BP category classification. In the proposed method, the pulse waves extracted from a Doppler radar are classified into three categories, 'Low BP', 'Normal BP', and 'High BP' by k-Nearest Neighbor (kNN) based on the features that correlate with BP. The SBP estimation model is trained for each BP category. After the BP category classification, SBP is then estimated by using the model corresponding to the classified BP category. The experimental results showed that the proposed method with BP category classification estimated SBP accurately, compared to without BP category classification.

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