論文 - 大槻　知明

An Efficient Specific Emitter Identification Method Based on Complex-Valued Neural Networks and Network Compression

Wang Y., Gui G., Gacanin H., Ohtsuki T., Dobre O.A., Poor H.V.

IEEE Journal on Selected Areas in Communications （IEEE Journal on Selected Areas in Communications）  39 （ 8 ） 2305 - 2317 2021年08月

ISSN  07338716

　概要を見る

Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% sim 30 % model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.

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Federated learning for DL-CSI prediction in FDD massive MIMO systems

Hou W., Sun J., Gui G., Ohtsuki T., Elbir A.M., Gacanin H., Sari H.

IEEE Wireless Communications Letters （IEEE Wireless Communications Letters）  10 （ 8 ） 1810 - 1814 2021年08月

ISSN  21622337

　概要を見る

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, deep learning for predicting the downlink channel state information (DL-CSI) has been extensively studied. However, in some small cellular base stations (SBSs), a small amount of training data is insufficient to produce an excellent model for CSI prediction. Traditional centralized learning (CL) based method brings all the data together for training, which can lead to overwhelming communication overheads. In this work, we introduce a federated learning (FL) based framework for DL-CSI prediction, where the global model is trained at the macro base station (MBS) by collecting the local models from the edge SBSs. We propose a novel model aggregation algorithm, which updates the global model twice by considering the local model weights and the local gradients, respectively. Numerical simulations show that the proposed aggregation algorithm can make the global model converge faster and perform better than the compared schemes. The performance of the FL architecture is close to that of the CL-based method, and the transmission overheads are much fewer.

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From Multiset Events to Signal Restoration via Tensor Decomposition Based Separation Learning

Wang L., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2021年06月

ISSN  15503607

　概要を見る

The vision and development of blind source separation (BSS) considering the multi-dimensional components attract growing interests in the research community. The single-set BSS methods cannot address the challenge of aligning the extracted components across different datasets, because they neglect the dependence information across datasets. In this paper, we propose to apply the tensor theory to develop a novel structure for the BSS technique that allows us to discover the multi-set components and meaningful spatio-temporal structures of data. The structure is stacked of covariance matrices with temporal information, in terms of limited storage, using the tensor theory that can efficiently encapsulate and compress large-scale data into a compact format. In addition, we discuss how our results provide unique decomposition under such conditions from the theoretical perspective. The experiments designed on the signal with time variation (multiset), which is not identifiable when each mixture is considered individually. It can be solved to restate as a tensor structure of their spatial and temporal correlation matrices. Compared with the other three classical algorithms, the proposed algorithm consistently shows high accuracy.

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Non-contact Blood Pressure Measurement using Doppler Radar based on Waveform Analysis by LSTM

Ishizaka S., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2021年06月

ISSN  15503607

　概要を見る

Recently, non-contact blood pressure measurement is attracting increasing interests because it is suitable for the blood pressure measurement on a daily basis. A Doppler radar is a key device to enable the non-contact blood pressure measurement, and it can observe pulse waves due to aortic vasomotion. Specifically, PTT (Pulse Transit Time) is known to be correlated with the blood pressure, and thus the blood pressure can be measured by calculating the PTT from the pulse waves. However, to avoid the effects of slight body movements and breathing, the conventional method requires the subject to lie down on a bed and to stop breathing. In this paper, we propose a Doppler radar-based blood pressure measurement method that works against a subject with slight body movements and breathing. Due to respiration and slight body movements, the pulse wave could be distorted, which makes it difficult to calculate PTT. To solve this issue, in the proposed method, by relating features of the distorted pulse wave to those of the clean pulse wave using a deep learning model with LSTM (Long Short-Term Memory), the distorted pulse wave is transformed to a clean pulse wave that provides PTT correlated with the blood pressure strongly. The experimental results showed that PTT calculated using the proposed method has a strong correlation with actual blood pressure and the accuracy of blood pressure estimation is improved, compared with the conventional method.

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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年06月

ISSN  15361276

　概要を見る

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年06月

ISSN  15503607

　概要を見る

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年05月

　概要を見る

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年05月

ISSN  00189480

　概要を見る

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年04月

　概要を見る

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年01月

　概要を見る

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年01月

ISSN  00189545

　概要を見る

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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Discrete BP Polar Decoder Using Information Bottleneck Method

Yamada A., Ohtsuki T.

IEEE Access （IEEE Access）  9   10645 - 10656 2021年

　概要を見る

Polar code is one of the channel codes and is used in the 5th generation of mobile communication system (5G). This encoding scheme is based on the operation of channel polarization, and it is possible to achieve the capacity of arbitrary binary-input symmetric discrete memoryless channels. Compared with Turbo codes and LDPC (Low-Density Parity-Check) codes, the implementation of the encoder and decoder is easier. BP (Belief Propagation) decoding, one of the decoding methods of the polar codes, can be performed at high speed because it can decode in parallel. However, the disadvantages of the BP decoding are the hardware and computational complexities. The technique of quantization can be used to reduce complexities of hardware and calculation. One of the quantization methods is the information bottleneck method, which allows an observation variable to compressed one while trying to preserve the mutual information shared with a relevant variable. As a novel approach, the information bottleneck method is used in the design of quantizers for the BP decoding of LDPC codes. In this paper, we propose a discrete BP polar decoder that can use only unsigned integers in the decoding process by using the information bottleneck method. Thus, we can replace complex calculations of BP decoding with simple lookup tables. We also investigate the minimum bit width for quantization with negligible degradation and the suboptimal Eb/N0 for designing lookup tables, where Eb and N0 denote energy per bit and noise power density, respectively. The simulation results show that the proposed method can achieve almost the same error correcting capability compared with the BP decoding without compression in the range of low Eb/N0. Besides, we show that the proposed decoder can compress both channel outputs and BP messages with small loss compared with the uniform quantization decoder.

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Differentiable architecture search-based automatic modulation classification

Wei X., Luo W., Zhang X., Yang J., Gui G., Ohtsuki T.

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

ISSN  15253511

　概要を見る

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.

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Deep Learning-Based Channel Estimation for Massive MIMO Systems with Pilot Contamination

Hirose H., Ohtsuki T., Gui G.

IEEE Open Journal of Vehicular Technology （IEEE Open Journal of Vehicular Technology）  2   67 - 77 2021年

　概要を見る

In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) is required to obtain accurate estimation of channel state information (CSI) for a user terminal (UT). Because of the time-varying nature of the channel, the length of pilot signals is limited and the number of orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by pilot contamination from the neighboring cells. The minimum mean square error (MMSE) channel estimation can be used to reduce the influence of pilot contamination. However, it needs to know the covariance matrix of channels for all the UTs, which is unknown to the BS in practice. In this paper, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. The former method is better in terms of the training speed, however, the latter one can estimate the channel more accurately. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and covariance estimation methods via normalized mean square error (NMSE). In addition, we also investigate the impact of channel aging, and show that including some expected data into training datasets can avoid the great degradation of estimation quality.

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Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular Communications

Zhou Y., Wangt Y., Liu P., Yang J., Ohtsuki T., Sari H.

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

ISSN  15502252

　概要を見る

Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.

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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年

　概要を見る

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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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年

ISSN  15502252

　概要を見る

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年

ISSN  1557170X

　概要を見る

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年

　概要を見る

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年

　概要を見る

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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