論文 - 大槻　知明

Optimal Sleep Wakeup Mechanism for Green Internet of Things

Panahi F.H., Panahi F.H., Heshmati S., Ohtsuki T.

ICEE 2019 - 27th Iranian Conference on Electrical Engineering （ICEE 2019 - 27th Iranian Conference on Electrical Engineering）     1659 - 1663 2019年04月

ISSN  9781728115085

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© 2019 IEEE. The presence of many battery-powered sensors in the Internet of Things (IoT) paradigm calls for the design of green and energy-aware protocols. One of the major challenges in the wireless networks based on IoT is to reduce energy consumption and increase network lifetime. This paper addresses the problem of designing a smart sleep/wake-up mechanism using the genetic algorithm (GA). GA, as a nature-inspired optimization approach, is used to manage the operation modes of the base stations (BSs) according to the time-variant profile of the daily traffic load. Accordingly, when a number of stations are sending data to nodes, i.e., active or awake nodes, other nodes are in inactive (sleep) mode. In this case, the energy efficiency (EE) of the network increases to an acceptable level via controlling the operation modes of the densely deployed BSs over wireless sensor networks (WSNs).

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A Convergence and Asymptotic Analysis of Nonlinear Separation Model

Wang L., Ohtsuki T.

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings （2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings）     963 - 971 2019年03月

ISSN  9789881476852

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© 2018 APSIPA organization. Nonlinear blind source separation is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. The key idea is to recover the sources by estimating an approximation function so as to approximate the inverse of mixing function. However, in practice, the approximation function is derived from some estimation algorithm with finite sample size, which leads to the performance loss. In this paper, we work on the convergence and asymptotic analysis of the separation approach, which uses the flexible approximation to extract the nonlinearity of mixture function so that to make the problem linearly separable. The analysis stems from the performance of a mismatched estimator that accesses the finite sample size. By providing a closed-form expression of normalized mean squared error (NMSE), we can present a novel algebraic formalization that leads to the upper bound on the estimation error. The simulation results show that if the flexible approximation can extract the nonlinearity of mixing functions, the minimized NMSE can be achieved as the sample size tends to be infinity. This implies that the algorithm is feasible to separate the distortion of the nonlinear mixture.

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Time series analysis for bitcoin transactions: The case of Pirate@40's HYIP scheme

Toyoda K., Ohtsuki T., Mathiopoulos P.

IEEE International Conference on Data Mining Workshops, ICDMW （IEEE International Conference on Data Mining Workshops, ICDMW）  2018-November   151 - 155 2019年02月

ISSN  9781538692882

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© 2018 IEEE. Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a 'static' manner and none of them analyzes transaction history 'dynamically', i.e. without taking into account the 'time variation of how Bitcoin is transferred'. The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.

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A Novel Methodology for HYIP Operators' Bitcoin Addresses Identification

Toyoda K., Takis Mathiopoulos P., Ohtsuki T.

IEEE Access （IEEE Access）  7   74835 - 74848 2019年

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© 2013 IEEE. Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.

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Underdetermined Blind Source Separation with Multi-Subspace for Nonlinear Representation

Wang L., Ohtsuki T.

IEEE Access （IEEE Access）  7   84545 - 84557 2019年

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© 2013 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-subspace structure and sparse representation in the time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an optimization problem on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture of TF vectors with the fewer computational cost. The experiments are designed on the observations that are generated from an underdetermined mixture, and that is collected with some direction angles in a virtual room environment. The proposed approach exhibits higher separation accuracy.

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Multiple Radios for Fast Rendezvous in Heterogeneous Cognitive Radio Networks

Li A., Han G., Ohtsuki T.

IEEE Access （IEEE Access）  7   37342 - 37359 2019年

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© 2013 IEEE. In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

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Estimation of stress condition of children by nasal skin temperature

Suzuki M., Ohtsuki T.

Advances in Intelligent Systems and Computing （Advances in Intelligent Systems and Computing）  857   1351 - 1358 2019年

ISSN  9783030011765

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© Springer Nature Switzerland AG 2019. In Japan a lot of children stay home alone after school. Safety of children is a big issue. In addition, a stability of their emotion is a big issue. For the stability of their emotion, it is important to know the psychological state of children, particularly stress so that we can try to relieve their stress. As for the detection of the stress, it is preferred to detect in a less constrained or non-contact way. The nasal skin temperature is known to be affected by emotion, which can be detected in a non-contact way with an infrared thermography camera. In this paper, we report our experimental results about the detection of stress condition of children by the nasal skin temperature with the infrared thermography camera. In estimating the stress condition, we use a heart rate variability as a physiological index. With our experimental results, we show that the stress condition of children can be estimated by the nasal skin temperature. We also show that when the stress index rises, the change of the nasal skin temperature differs depending on their ages.

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Doppler Sensor-Based Blink Duration Estimation by Analysis of Eyelids Closing and Opening Behavior on Spectrogram

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE Access （IEEE Access）  7   42726 - 42734 2019年

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© 2013 IEEE. Blink duration is one of the useful indicators to estimate drowsiness and fatigue. A Doppler sensor could be a key device to realize the non-contact blink duration estimation, which is very useful for the drowsiness and fatigue monitoring in real life. However, none of the blink duration estimation methods has been proposed so far. In this paper, we propose a novel Doppler sensor-based blink duration estimation method based on the analysis of eyelids closing and opening behavior on a spectrogram. When one blinks, the energies caused by eyelids closing and opening behavior appears on a spectrogram. Hence, the blink duration can be estimated by integrating such energies and then detecting the timings when the integrated energy caused by the eyelids closing behavior appears and when the energy caused by the eyelids opening behavior disappears. To precisely detect such timings, a spectrogram with a short time window is better. However, thorough the preliminary experiments, we confirmed that when a shorter time window is used, more noise appears around such timings, which makes it difficult to detect such timings. To deal with this issue, the proposed method multiplies several integrated energies for spectrograms with different time windows. By multiplying the integrated energies with such noise and the ones without such noise, the energy level of such noise gets significantly declined over the product. Furthermore, the levels of the redundant blink energies also decline to the noise level, where the redundant blink energies denote the ones that appear before and after the eyelids closing and opening timings because of a long time window. The results of the experiments on 8 subjects watching a movie showed that our method achieved an average root mean squared error (RMSE) of 49.3 ms.

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Precoding aided generalized spatial modulation with an iterative greedy algorithm

Yuwen Cao, Tomoaki Ohtsuki

IEEE Access （IEEE Access）  6   72449 - 72457 2018年11月

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© 2018 IEEE. This paper divides antennas at the transmitter into several transmit antenna groups (TAGs) and extends the generalized spatial modulation (GSM) idea to a new precoding-aided multiple-input multiple-output system, which is referred to as precoding-aided GSM (PGSM) system. To mitigate the influence caused by correlated channels, we consider the interleaved grouping scheme which leads to a minimum antenna correlation within the same TAG. To select the receive antenna subset with low complexity, we propose an iterative greedy (IG) algorithm that contributes to a maximum equivalent channel gain with low-computational complexity. To reduce the complexity of maximum likelihood (ML) detector, a low complexity IG algorithm-based ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with an ordered block minimum mean-squared error detector at a given BER of 10-4. It is also shown that the proposed PGSM with the IG-ML detector achieves a similar BER performance in a high signal-to-noise power ratio region compared with that of an ideal precoding for the GSM with an optimal ML detector.

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Smart Image-Processing based Energy Harvesting for Green Internet of Things

Panahi F.H., Hajimirzaee P., Erfanpoor S., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

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© 2018 IEEE. Internet of Things (IoT), as a widespread growing technology which connects various heterogeneous devices of wireless sensor networks (WSNs), plays a great role in sensing, monitoring, controlling of the covering environment. However, maximizing the lifetime of WSNs is still a major challenge. Although some approaches have been introduced to overcome the vital problem so far, research on this problem experiences a slow progress. Inspired by the promising performance of fuzzy-based Q-learning (FQL) algorithm to design practical smart sensors, this paper proposes a FQL-based approach that can maximize the lifetime of sensors and accelerate the process of wireless energy harvesting (EH) for mobile sensors which coexist with macro and small base stations (SBSs) deployed over a time-variant heterogeneous network (HetNet). The methodology is based on a centralized image-processing (IP) approach to scan and find the instant coverage map of the HetNet and then to localize red regions, i.e., regions with high levels of energy. Furthermore, mobile sensors attempt to access these regions during frequent movements. This will help to maximize the lifetime of the WSN. Simulation results confirm the effectiveness of the wireless EH process and smart aggregation of mobile sensors around the dense-coverage areas.

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Smart Energy Harvesting for Internet of Things

Panahi F.H., Moshirvaziri S., Mihemmedi Y., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

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© 2018 IEEE. In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.

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D2D-aided Optimal Utilization of Renewable Energies for Green Powered Base Stations

Panahi F.H., Moshirvaziri S., Momene A., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

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© 2018 IEEE. In this paper, due to the unlimited growth of service demands and high load traffic, a green wireless communication system has been considered with the aim of decreasing energy consumption of heterogeneous networks (HetNets). This system provides a proportion of required energy of Base Stations (BSs) by employing some renewable power suppliers (RPSs), while user equipments (UEs) especially benefit from short device-to-device (D2D) links as a promising technique to design energy-efficient HetNets. Indeed, we attempt to focus on efficient interaction between the RPS's random green power generations and the BS's dynamic of energy consumption via minimization of an energy-based cost function, considering the D2D impacts on efficient utilization of energy and bandwidth. Simulation results validate that the proposed framework provides significant improvements in electrical grid power-consumption and total energy efficiency (EE) of the network.

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Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation Technique

Ye C., Toyoda 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）  2018-July   1 - 6 2018年10月

ISSN  9781538636466

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© 2018 IEEE. Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.

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A Stochastic Gradient Approach for Robust Heartbeat Detection With Doppler Radar Using Time-Window-Variation Technique

Ye C., Toyoda K., Ohtsuki T.

IEEE Transactions on Biomedical Engineering （IEEE Transactions on Biomedical Engineering）  66 （ 6 ） 1730 - 1741 2018年10月

ISSN  00189294

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© 2019 IEEE. Heart rate (HR) variability indicates health condition and mental stress. The development of non-contact HR monitoring techniques with Doppler radar is attracting great attention. However, the performance of heartbeat detection via radar signals easily degrades due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct the high-resolution spectrum of heartbeat by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function and penalize the sparse constraint on updating the spectrum, a more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises of different strengths caused by subjects' movements, an adaptive regularization parameter is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of the location of the spectral peak associated with the HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement in accuracy compared with existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute when subjects type on a laptop.

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Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  6   64486 - 64502 2018年10月

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© 2018 IEEE. With the rapid growth of online social media content, and the impact these have made on people's behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as 'quantification.' By the term 'quantification,' we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.

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Spectrogram-Based Non-Contact RRI Estimation by Accurate Peak Detection Algorithm

Kohei Yamamoto, Kentaro Toyoda, Tomoaki Ohtsuki

IEEE Access （IEEE Access）  6 （ 1 ） 60369 - 60379 2018年10月

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© 2013 IEEE. Demands for vital sign monitoring are increasing in the field of health care. In particular, the R-R Interval (RRI) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor-based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor-based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the root-mean-squared error of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index low-frequency/high-frequency (LF/HF), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the relative error of the LF/HF.

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On Physical Layer Security of α-η-κ-μ Fading Channels

Mathur A., Ai Y., Bhatnagar M., Cheffena M., Ohtsuki T.

IEEE Communications Letters （IEEE Communications Letters）  22 （ 10 ） 2168 - 2171 2018年10月

ISSN  10897798

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© 1997-2012 IEEE. In this letter, we study the secrecy performance of the classic Wyner's wiretap model, where the main and eavesdropper channels are modeled by a general and versatile α-η-κ-μ fading model. Novel and exact expressions of the average secrecy capacity and secrecy outage probability have been derived. Previous results on physical layer security can be obtained through our newly derived expressions by specializing the model parameters. More importantly, the derived results are also applicable for the secrecy performance analysis of some field measurements (e.g., in millimeter wave communications), which cannot be analyzed by previous results.

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Recovery of block-structured sparse signal using block-sparse adaptive algorithms via dynamic grouping

Ye C., Gui G., Xu L., Ohtsuki T.

IEEE Access （IEEE Access）  6   56069 - 56083 2018年09月

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© 2013 IEEE. A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block ℓ0-norm LMS (B ℓ0-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B ℓ0-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.

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Doppler Analysis Based Fall Detection Using Array Antenna

Agata Y., Ohtsuki T., Toyoda K.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2018-May 2018年07月

ISSN  9781538631805

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© 2018 IEEE. The number of elderly people who live alone is increasing in many countries. Furthermore, many of their accidents occur at home. Hence, it is an urgent demand for a system monitoring their activities to detect accidents indoor such as falling. In conventional systems of fall detection using array antennas, falling after standing still can be detected with high accuracy by leveraging the features indicating the change of radio wave propagation. However, it is difficult to detect falling after walking correctly. In this paper, to improve fall detection accuracy including falling after walking, we propose an accurate fall detection system by leveraging the features indicating the change of Doppler signals during human activities in detail. Analyzing Doppler signals is useful to detect falling since they are observed when a radio wave reflects by moving objects. We conducted experiments in actual rooms to demonstrate that the proposed method can detect falling after both standing and walking with high accuracy.

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Robust Heartbeat Detection with Doppler Radar Based on Stochastic Gradient Approach

Ye C., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2018-May 2018年07月

ISSN  9781538631805

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© 2018 IEEE. Heart-rate variability (HRV) is closely related with physical or mental conditions. It is rather necessary to develop remote monitoring technologies to reduce subjects' pressure, e.g., heart-rate (HR) monitoring to drivers. Recently, the contactless heartbeat detection with Doppler radar has drawn extensive attention, due to less burden on subjects. However, the received signals of Doppler radar are easily contaminated by respiration or body motion, resulting in performance degradation. In this paper, to realize robust heartbeat detection with Doppler radar, a stochastic gradient approach is first proposed to reconstruct heartbeat spectrum. By correcting the gradient of cost function constructed by recursion error, and utilizing the sparse characteristics of heartbeat spectrum, the reconstructed spectrum can be obtained with minimum deviation. Furthermore, the zero-attracting sign least mean square (ZA- SLMS) algorithm based on stochastic gradient descent (SGD) is proposed, to accomplish more stable sparse spectrum reconstruction (SSR), by quantizing the updating of recursion error. Finally, HR is estimated by spectral peak tracking consisting of peak selection and verification. Experimental results validate that the proposed method can significantly improve detection accuracy over the conventional methods, based on the measurements from 5 subjects during sitting still or typing with a laptop.

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