Details of a Researcher - Ohtsuki, Tomoaki

Papers - Ohtsuki, Tomoaki

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Multi-Task Learning for Generalized Automatic Modulation Classification under Non-Gaussian Noise with Varying SNR Conditions

Wang Y., Gui G., Ohtsuki T., Adachi F.

IEEE Transactions on Wireless Communications (IEEE Transactions on Wireless Communications) 20 ( 6 ) 3587 - 3596 2021.06

ISSN 15361276

Automatic modulation classification (AMC) is a critical algorithm for the identification of modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Deep learning (DL)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work under the corresponding condition. In this paper, a novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from knowledge-sharing-based MTL in varying noise scenarios. In detail, multiple CNN models with the same structure are trained for multiple SNR conditions, but they share their knowledge (e.g. model weight) with each other. Thus, MTL can extract the general features from datasets in different noise scenarios. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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Mobility-Aware Routing and Caching: A Federated Learning Assisted Approach

Cao Y., Maghsudi S., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2021.06

ISSN 15503607

We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs' preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation.1 Theoretical and numerical analyses show the effectiveness of our proposed approach.

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Distributed Deep Reinforcement Learning for Renewable Energy Accommodation Assessment with Communication Uncertainty in Internet of Energy

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

IEEE Internet of Things Journal (IEEE Internet of Things Journal) 8 ( 10 ) 8557 - 8569 2021.05

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

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Spectral Viterbi Algorithm for Contactless Wide-Range Heart Rate Estimation with Deep Clustering

Ye C., Ohtsuki T.

IEEE Transactions on Microwave Theory and Techniques (IEEE Transactions on Microwave Theory and Techniques) 69 ( 5 ) 2629 - 2641 2021.05

ISSN 00189480

Objective: The main challenge in contactless heartbeat detection comes from breathing and/or body motion, which typically deteriorate heart rate (HR) measurements, due to incorrect selection of spectral peaks associated with HR. To acquire the reliable peak selection on spectrum within a relatively broad range, this article first proposes a spectral Viterbi algorithm. Second, a nonlinear source separation approach is further proposed to eliminate the noises generated by respiration and movements, suppressing the undesired spectral energy. Proposal: Inspired by the fact that the period of peak-to-peak intervals of heartbeat (RRIs) rarely vary within a short duration, a novel spectral Viterbi algorithm is proposed to estimate HR change, by the path metric (PM) of candidate paths of HR change. Moreover, based on a deep recurrent neural network (RNN), deep clustering (DC) is applied to separate out the targeted heartbeat source from Doppler signal, by dividing its spectrogram. Results: On the premise of wide-range HR measurement, the usage of spectral Viterbi algorithm substantially improved the precision compared with typical methods of HR estimation, both in the statuses of human subjects' sitting still and typewriting. In addition, the combination of DC obtains the smallest average errors. Significance: The proposed spectral Viterbi algorithm with DC is provided with three main strengths: 1) good adaptability to wide-range HR change; 2) robustness to nonlinearly mixed signal and noises; and 3) requirement of only a single-channel sensor.

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Spectrum-aware energy efficiency analysis in K-tier 5G hetnets

Panahi F.H., Panahi F.H., Ohtsuki T.

Electronics (Switzerland) (Electronics (Switzerland)) 10 ( 7 ) 2021.04

In future multi-tier cellular networks, cognitive radio (CR) compatible with device-to-de-vice (D2D) communication can be an aid to enhance system spectral efficiency (SE) and energy efficiency (EE). Users in proximity can establish a direct connection with D2D communication and by-pass the base stations (BSs), thereby offloading the network infrastructure and providing EE im-provement. We use stochastic geometry to model and analyze cognitive D2D communication underlying a multi-tier/multi-channel cellular network where the D2D transmitters are capable of harvesting RF energy from ambient interference resulting from simultaneous cellular downlink trans-missions. For further improvement in EE, small cells (SCs) can be put into a power-saving mode by specifying a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, to con-sider practical D2D communication scenarios, we propose a wireless video sharing framework where cache-enabled users can store and exchange popular video files through D2D communica-tion. We investigate the potential effects of the TPC and the introduced D2D layer on the network EE and SE. We will also observe that the energy-harvesting CR-based D2D communication network design will not only ease the spectrum shortage problem but will also result in a greener network thanks to its reliance on ambient energies.

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All-in-One Hate Speech Detectors May not be what You Want

Bouazizi M., Niida N., Ohtsuki T.

ACM International Conference Proceeding Series (ACM International Conference Proceeding Series) 165 - 170 2021.01

The detection of Hate speech has been an increasingly active research topic. The results reported by the state-of-the-art systems to automatically detect hateful contents achieved almost perfect performance on common data sets. However, "hate speech"is a very subjective term, and people with different backgrounds have different levels of tolerance to what constitutes hate. In this paper, we show the limitations of having a single classifier handling the problem of hate speech detection. We then propose to build classifiers customized for different people, instead of a single classifier. The main obstacle towards achieving such a goal is the scarcity of data. Therefore, we use transfer learning to overcome this issue and use very limited amount of annotated data to build these customized classifiers. In a first stage, we build a classifier on a large data set which classifies tweets into 3 classes: hate, offensive, clean, and which we refer to as the general classifier. In the second stage, we asked 3 annotators with different backgrounds to re-annotate a small sub-set of tweets (600 tweets) from the original one. We refer to this newly created data set as "the customized data set."We then fine-tune the general classifier on the customized data set and build the customized classifier for each annotator. The accuracy of classification of corresponding customized data set got 0.08, 0.06 and 0.11 higher than the general classifier. The result shows that it is possible to start with a general classifier, and adjusted it to each individual despite the very limited amount of the training data for him/her.

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A Deep Learning-Based Low Overhead Beam Selection in mmWave Communications

Echigo H., Cao Y., Bouazizi M., Ohtsuki T.

IEEE Transactions on Vehicular Technology (IEEE Transactions on Vehicular Technology) 70 ( 1 ) 682 - 691 2021.01

ISSN 00189545

Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave) technology has gained extensive research attention in 5G communications, whereas mmWave links suffer from severe free space attenuation. Codebook-based beamforming techniques with multiple antennas can effectively alleviate this challenge with low computational complexity and low hardware cost. However, small delay and high-speed communications with beamforming techniques require beam alignment with small overhead so as to establish the wireless link quickly. In this context, this paper proposes a deep learning-based low overhead analog beam selection scheme by virtue of the super-resolution technology. To be concrete, deep neural networks are employed to conduct beam quality estimation based on partial beam measurements. Our proposed scheme can cover all the directions of arriving signals with low overhead by utilizing codebooks with different beam widths. Furthermore, for the purpose of further reducing the overhead, we formulate the beam quality prediction model based on the past beam sweepings. With these beam quality estimation and prediction model, the beam that achieves large signal-to-noise-power-ratio (SNR) can be selected based on partial beam measurements. Simulation results show that the proposed scheme can accurately estimate beam qualities and give high probability of optimal beam selections with low overhead.

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An Effective Radar Signal Recognition Method Using Neural Architecture Search

Zhang M., Luo W., Wang Y., Sun J., Yang J., Ohtsuki T.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.

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An Autoencoder-Based Fetal Heart Rate Detector for Noninvasive Recordings

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

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) 60 - 63 2021

ISSN 1557170X

Antenatal fetal health monitoring primarily depends on the signal analysis of abdominal or transabdominal electrocardiogram (ECG) recordings. The noninvasive approach for obtaining fetal heart rate (HR) reduces risks of potential infections and is convenient for the expectant mother. However, in addition to strong maternal ECG presence, undesirable signals due to body motion activity, muscle contractions, and certain bio-electric potentials degrade the diagnostic quality of obtained fetal ECG from abdominal ECG recordings. In this paper, we address this problem by proposing an improved framework for estimating fetal HR from non-invasively acquired abdominal ECG recordings. Since the most significant contamination is due to maternal ECG, in the proposed framework, we rely on neural network autoencoder for reconstructing maternal ECG. The autoencoder endeavors to establish the nonlinear mapping between abdominal ECG and maternal ECG thus preserving inherent fetal ECG artifacts. The framework is supplemented with an existing blind-source separation (BSS) algorithm for post-treatment of residual signals obtained after subtracting reconstructed maternal ECG from abdominal ECG. Furthermore, experimental assessments on clinically-acquired subjects' recordings advocate the effectiveness of the proposed framework in comparison with conventional techniques for maternal ECG removal.

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Activity Detection using 2D LIDAR for Healthcare and Monitoring

Bouazizi M., Ye C., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings (2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings) 2021

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term 'activity' to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3% and 91.3% in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people's gait with an accuracy equal to 92.4% using 10 seconds of walking data.

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A Novel Hybrid Deep Learning Model for Activity Detection Using Wide-Angle Low-Resolution Infrared Array Sensor

Muthukumar K.A., Bouazizi M., Ohtsuki T.

IEEE Access (IEEE Access) 9 82563 - 82576 2021

In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) array sensors. Alongside with the main challenge which is how to further improve the performance of IR array sensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR array sensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors ( 32\times24 pixels), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including 'walking', 'standing', 'sitting', 'lying', 'falling', and the transition between the activities which is referred to as 'action change'. This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of activities. Through experiments, we show that the classification accuracy of the ceiling data, wall data, and combined data with the LSTM reach 0.96, 0.95, and 0.97, respectively.

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A novel compression CSI feedback based on deep learning for FDD massive MIMO systems

Wang Y., Zhang Y., Sun J., Gui G., Ohtsuki T., Adachi F.

IEEE Wireless Communications and Networking Conference, WCNC (IEEE Wireless Communications and Networking Conference, WCNC) 2021-March 2021

ISSN 15253511

Accurate channel state information (CSI) is necessary for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. Existing deep learning-based CSI feedback methods, e.g., CSI sensing and recovery neural network (CsiNet), designed based on an autoencoder architecture, achieves higher feedback accuracy and reconstruction speed. However, this network needs to be retrained due to different communication scenarios and channel conditions, which is costly in practical deployment. To solve this problem, this paper proposes a deep learning-based modular adaptive multiple-rate (MAMR) compression CSI feedback framework. Extra padding modules are added at the base station to pad compressed CSI into different compression rates into the same dimensions, thereby realizing a general autoencoder performing variable-rate compression. Simulation results are given to confirm the effectiveness of the proposed method in terms of normalized mean square error.

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A Novel Approach for Inter-User Distance Estimation in 5G mmWave Networks Using Deep Learning

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

Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021 (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021) 223 - 228 2021

Accurate localization of devices in 5G cellular networks is of that utmost importance. This is because location information is a key component of a variety of new emerging applications. In particular, collocation (or co-location) refers to the idea of identifying devices that are located within a certain range from one another. In this paper, we propose a novel technique for inter-user distance estimation that uses low-resolution and high-resolution beam energy-based images as location fingerprints. Our approach uses the beam energy-based images generated by different users to estimate the distance between each pair of them. Nevertheless, we explore the idea of using a deep learning technique referred to as super resolution applied on low-resolution beam energy-based images to enhance their resolution, thus identify collocated users with an accuracy comparable to that of higher resolution ones. More specifically, throughout our experiments, we generate images of resolution 4times 4 and 8times 8 and use these for distance estimation between users. Afterwards, we apply super resolution on images with size 4times 4 to improve their resolution, and compare their results to the ones obtained with the original 8times 8 images. For an area roughly equal to 60times 30 mathrm{m}, our proposed approach reaches an average mean squared error equal to 0.13 m. We also demonstrate how our proposed approach outperforms the conventional ones that rely on user location detection to measure the inter-user distance.

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A novel approach based on lightweight deep neural network for network intrusion detection

Zhao R., Li Z., Xue Z., Ohtsuki T., Gui G.

IEEE Wireless Communications and Networking Conference, WCNC (IEEE Wireless Communications and Networking Conference, WCNC) 2021-March 2021

ISSN 15253511

With the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.

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Data Augmentation for Sentiment Analysis Using Sentence Compression-Based SeqGAN with Data Screening

Luo J., Bouazizi M., Ohtsuki T.

IEEE Access (IEEE Access) 9 99922 - 99931 2021

Sentiment analysis refers to the process of automatically identifying the emotions expressed by people. Its accuracy is highly dependent on the amount of training data. However, it takes time and cost for humans to collect a large number of data. Many research works used generative models to generate a large amount of data based on a small amount of data for sentiment analysis. However, training on long texts and inaccurate sentiment information that might be generated are two severe challenges. It is difficult to improve the sentiment analysis accuracy effectively. In this paper, we propose a novel data augmentation framework based on Sequence generative adversarial networks (SeqGAN) to improve the sentiment analysis accuracy when the dataset already has a certain amount of data and contains long texts. Penalty-based SeqGAN is used to generate high-quality and diversified text data. Long short-term memory (LSTM) networks with attention mechanisms are used to conduct sentence compression for the training data of SeqGAN. A sentiment dictionary is used to retain the sentiment words for compressed data. We also propose a data screening method to obtain more accurate data from the generated data. The results of the usability, novelty, and diversity of the generated data show that the proposed sentence compression method can help SeqGAN learn more information from the long text data. The data generated by the proposed framework improve the classification accuracy of four classifiers applied on two distinct text datasets.

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Weighted-Beam Superposition for mmWave Massive MIMO-NOMA Systems

Dai H., Yin Y., Huang H., Yang J., Ohtsuki T., Sari H., Adachi F.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

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Spatial degrees of freedom exploration and analog beamforming designs for signature spatial modulation

Cao Y., Ohtsuki T.

IEICE Transactions on Communications (IEICE Transactions on Communications) E104B ( 8 ) 934 - 941 2021

ISSN 09168516

In this paper, we focus on developing efficient multi-configuration selection mechanisms by exploiting the spatial degrees of freedom (DoF), and leveraging the simple design benefits of spatial modulation (SM). Notably, the SM technique, as well as its variants, faces the following critical challenges: (i) the performance degradation and difficulty in improving the system performance for higher-level QAM constellations, and (ii) the vast complexity cost in precoder designs particularly for the increasing system dimension and amplitude-phase modulation (APM) constellation dimension. Given this situation, we first investigate two independent modulation domains, i.e., the original signal- and spatial-constellations. By exploiting the analog shift weighting and the virtual spatial signature technologies, we introduce the signature spatial modulation (SSM) concept, which is capable of guaranteing superior trade-offs among spectral- and cost-efficiencies, and system bit error rate (BER) performance. Besides, we develop an analog beamforming for SSM by solving the introduced unconstrained Lagrange dual function minimization problem. Numerical results manifest the performance gain brought by our developed analog beamforming for SSM.

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Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar

Yamamoto K., Endo K., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings (2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings) 2021

Remote sensing of heartbeat offers various applications in the medical and health care fields. To realize non-contact heartbeat detection, an FMCW (Frequency Modulated Continuous Wave) radar-based heartbeat detection method has been investigated. The conventional FMCW radar-based heartbeat detection method estimates a range from an FMCW radar to a subject and extracts heartbeat components from phase changes for the range. However, the range suitable for extracting heartbeat components can change over time due to respiration and body fluctuation. Thus, when the SNR (Signal-to-Noise Ratio) of heartbeat components over phase changes is low at the estimated range, the accuracy of heartbeat detection tends to degrade. In this paper, we propose a MIMO (Multiple-Input Multiple-Output) FMCW radar-based heartbeat detection method based on space diversity. A MIMO FMCW radar can estimate the range for multiple beam directions and obtain phase changes for a space specified with the range and the beam direction. The SNR of heartbeat components over phase changes differs from one space to another. Taking it into account, the proposed method detects heartbeat by exploiting the space diversity of phase changes. The experimental results showed that compared to the detection method using only one phase change, the proposed method using phase changes for multiple spaces detected heartbeat accurately, which is brought by the diversity effect of phase changes for multiple spaces.

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Overloadedwireless mimo switching for information exchanging through untrusted relay in secure wireless communication

TAKAHASHI A., TAKYU O., FUJIWARA H., FUJII T., OHTSUKI T.

IEICE Transactions on Communications (IEICE Transactions on Communications) E104B ( 10 ) 1249 - 1259 2021

ISSN 09168516

Information exchange through a relay node is attracting attention for applying machine-to-machine communications. If the node demodulates the received signal in relay processing confidentially, the information leakage through the relay station is a problem. In wireless MIMO switching, the frequency spectrum usage efficiency can be improved owing to the completion of information exchange within a short time. This study proposes a novel wireless MIMO switching method for secure information exchange. An overloaded situation, in which the access nodes are one larger than the number of antennas in the relay node, makes the demodulation of the relay node difficult. The access schedule of nodes is required for maintaining the overload situation and the high information exchange efficiency. This study derives the equation model of the access schedule and constructs an access schedule with fewer time periods in the integer programming problem. From the computer simulation, we confirm that the secure capacity of the proposed MIMO switching is larger than that of the original one, and the constructed access schedule is as large as the ideal and minimum time period for information exchange completion.

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Noncontact heartbeat detection by Viterbi algorithm with fusion of beat-beat interval and deep learning-driven branch metrics

Yamamoto K., Ohtsuki T.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings) 2021-June 8308 - 8312 2021

ISSN 15206149

Heartbeat is one of essential vital signs to assess our health condition. Noncontact heartbeat detection is thus receiving a lot of attention in recent years, which motivates many researchers to investigate heartbeat detection via a Doppler radar. In this paper, to detect heartbeat with a high accuracy, we propose a Doppler radar-based heartbeat detection method by the Viterbi algorithm with a fusion of Beat-Beat Interval (BBI) and deep learning-driven Branch Metrics (BM). The Viterbi algorithm is a technique to estimate a sequence with maximum likelihood by using a pre-defined metric, namely, a BM. In the proposed method, we combine two BMs defined based on (i) a difference between two adjacent BBIs and (ii) an output probability of a deep learning model that judges whether a peak is caused by heartbeat or not. We apply the VIterbi algorithm with the fusion of the two BMs to the signal obtained by some signal processing. We experimentally confirmed that our method performed heartbeat detection with small Root Mean Squared Error (RMSE) between the estimated and actual BBIs.

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