論文 - 大槻　知明

Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment

Wang Y., Cai Z., Tang T., Ohtsuki T., Gui G.

IEEE Transactions on Information Forensics and Security 21   2641 - 2651 2026年

ISSN  15566013

　概要を見る

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

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Sum-Rate Maximization in Irregular STAR-RIS-Driven V2X Systems

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Wireless Communications Letters 15   1504 - 1508 2026年

ISSN  21622337

　概要を見る

The reconfigurable intelligent surface (RIS) plays a crucial role in enabling sustainable communication infrastructure for 6G networks. This letter presents a long-term net weighted sum-rate maximization problem with queue stability for irregular simultaneous transmission and reflection RIS (ISTAR-RIS)-assisted vehicular networks. The model incorporates time-varying and mobility-induced channel dynamics. A joint optimization problem of the road side unit (RSU) beamforming vector, topological selection and the phase shift matrix of ISTAR-RIS elements is formulated. The simulation results demonstrate the effectiveness of the proposed approach in maximizing the sum-rate while comparing with existing benchmark methods in ISTAR-RIS-aided wireless and device-to-device (D2D) communication.

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Ship Classification in SAR Images Based on Full-Attention ViT and Image Reconstruction

Wang L., Zhao C., Wang T., Sun B., Qi Y., Qi B., Ohtsuki T.

Lecture Notes in Electrical Engineering 1366 LNEE   501 - 509 2026年

ISSN  18761100

　概要を見る

Classification of ships in synthetic aperture radar (SAR) images plays a critical role in maritime search and rescue as well as national defense security. Over the past few years, the swift advancement rapid of deep learning has opened up innovative technological directions for precise classification of SAR ship images. However, the unique imaging mechanism of SAR images, resulting in a lack of ship samples and insufficient feature information, continues to make accurate classification of SAR ships challenging. In this study, we propose an improved vision transformer (ViT) method using a full attention (FA) mechanism, incorporating channel attention after Self-Attention, dynamically selecting channels through reweighting to enhance comprehensive feature representation. To address the issue of insufficient training samples, we designed an image reconstruction module based on SRGAN, which generates high-resolution and detailed image data through adversarial learning, effectively compensating for the sample quantity in the dataset. Ultimately, the results section demonstrates that the method introduced delivers enhanced classification accuracy relative to current methods.

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Self-Supervised Federated Learning for UAV-IoT Systems With Dynamic Non-IID Data via Model Correlation

Li Z., Bouazizi M., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 5827 - 5841 2026年

　概要を見る

Federated learning (FL) offers significant advantages in preserving data privacy and enhancing communication efficiency, making it especially suitable for Internet of Things (IoT) networks supported by uncrewed aerial vehicles (UAVs). However, most existing FL approaches rely on assumptions of uniformly distributed, well-labeled, and large-scale datasets—conditions that rarely hold in practical UAV-based IoT scenarios. These environments typically feature small-scale, nonindependent and identically distributed (non-IID), and dynamically changing data. To address these challenges, we propose a novel self-supervised federated unsupervised learning (FUL) framework tailored for UAV-assisted IoT systems. The proposed framework comprises three key components: 1) a realistic UAV data collection model that considers limited onboard storage and mobility constraints; 2) a robust local training strategy that incorporates self-supervised regularization and a centered kernel alignment (CKA)-based similarity loss to mitigate the effects of data heterogeneity and rapid distribution shifts; and 3) an importance-aware hybrid normalized aggregation method at the global server, which leverages model divergence-based metrics to evaluate local model reliability and integrates both current and historical gradient information for stable model updates. Experimental results demonstrate that our framework achieves classification accuracies of 30.5%, 62.8%, and 70.5% under memory constraints of 500, 1000, and 2000 samples, respectively—outperforming the best baseline by 18.1%, 26.5%, and 9.1% under the same conditions. These results highlight the effectiveness of the proposed FUL framework in handling data heterogeneity and dynamic sample variations inherent in realistic UAV-enabled IoT applications.

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R2D-SLAM: A Real-Time and Robust Visual SLAM Framework for Autonomous Navigation in Dynamic Environments

Sun J., Xutian Y., Shen X., Yin Y., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Classical Simultaneous Localization and Mapping (SLAM) algorithms, fundamental to autonomous navigation, often assume a static environment, an assumption that fails in many real-world scenarios, resulting in significant trajectory drift. This creates a long-standing trade-off in dynamic SLAM: high-accuracy methods are typically too slow for real-time deployment, while real-time systems often suffer from reduced robustness. To address this challenge, we propose R²D-SLAM, a novel real-time visual SLAM framework that achieves both high accuracy and efficiency in dynamic environments. Our system integrates a lightweight and effective dynamic object detector, Detection Transformer (DETR), with an enhanced matching strategy, termed DEIM_Dyna, into the robust ORB-SLAM3 framework. A dedicated parallel thread performs dynamic region detection concurrently with the main SLAM pipeline, enabling early rejection of contaminated features during tracking. This ensures that pose estimation is conducted on a purified, static feature set, significantly improving robustness and accuracy. Extensive experiments on the challenging TUM and BONN dynamic datasets show that R²D-SLAM achieves new state-of-the-art performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), while maintaining real-time processing speeds exceeding 25 FPS. R²D-SLAM effectively bridges the gap between accuracy and efficiency, offering a practical and robust solution for dynamic scene perception in autonomous vehicles and Unmanned Aerial Vehicles (UAVs).

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Optimal Carbon Emission Reduction Modeling Considering Energy Consumer Satisfaction in Cyber-Physical Energy System

Wang C., Guan X., Wang N., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 4 ） 6508 - 6521 2026年

　概要を見る

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response (DR) mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this article, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multicategory energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption pReferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes (MDPs). A model-free framework applying two state-of-the-art deep reinforcement learning (DRL) algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

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LRGD: Low-Rank Guided Diffusion for Robust Image Transmission in Semantic Communication

Zhao Z., Wu C., Lin Y., Zhong L., Ji Y., Ohtsuki T., Bennis M.

IEEE Transactions on Cognitive Communications and Networking 12   2439 - 2454 2026年

　概要を見る

Semantic communication has gained significant attention in modern data-intensive applications due to its efficient information transmission capabilities. While recent advancements in large language models and diffusion models have enabled generative image semantic communication in text-based and multimodal settings, existing approaches struggle with visual fidelity due to the inherent limitations of textual descriptions and the lack of dedicated design in multimodal methods. To address these issues, we propose a compressed sensing-driven multimodal semantic communication framework that transmits saliency-adaptive sparse sampling, edge contours, and textual descriptions of images at the transmitter and reconstructs images at the receiver using guided diffusion models. We innovatively design a low-rank guided diffusion (LRGD) model to adapt to this framework, employing a dynamic smoothing low-rank guidance process in the latent space using low-quality reconstructed images. This approach enables highly accurate restoration of visual details under a low sampling ratio without additional training. Compared to state-of-the-art methods, our framework uses model-independent and interpretable universal semantic representations for communication, significantly enhancing cross-model compatibility without model sharing between the transmitter and receiver. Extensive experiments on multiple datasets demonstrate that the proposed method achieves robust and superior image reconstruction performance across various noisy environments while compressing data to 5.7% of the original image size.

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Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Open Journal of the Communications Society 7   2446 - 2463 2026年

　概要を見る

In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions. In dynamic vehicular networks, irregular reconfigurable intelligent surface (IRIS) with non-uniformly distributed elements over a larger surface offer improved coverage, reduced interference, and lower power consumption. This article undertakes a rigorous investigation into IRIS-assisted downlink vehicular communication in the context of Doppler shift, with the objective of optimizing the long-term net weighted average sum-rate under both perfect and imperfect channel state information (CSI). This task entails a comprehensive joint optimization of IRIS topology selection, phase-shift matrix, and beamforming vectors, while ensuring queue stability. To address this stochastic network optimization problems involving time-varying system dynamics, a Lyapunov optimization-based framework is proposed. This framework transforms the stochastic problem into a deterministic form, ensuring queue stability without requiring knowledge of future channel conditions. Subsequently, a joint optimization strategy based on the block coordinate descent (BCD) algorithm is applied to make optimal decisions of IRIS topology selection, phase-shift matrix, and beamforming vectors in each time slot. To validate the effectiveness of the proposed framework, extensive simulations were conducted and benchmarked against two established schemes: one employing conventional IRIS configurations in wireless networks and the other utilizing non-orthogonal multiple access (NOMA) techniques. The results consistently demonstrate that the proposed IRIS-assisted Lyapunov optimization framework significantly outperforms both benchmarks in terms of achievable sum-rate and maintains queue stability.

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Lightweight Regularized Network for Multilabel Indoor HAR in Multiuser CSI Environments With Uncertainty Quantification

Miao F., Liu C., Lu Z., Shan L., Takyu O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 6475 - 6484 2026年

　概要を見る

Human activity recognition (HAR) with Wi-Fi channel state information (CSI) is attractive for privacy-preserving, device-free sensing; yet, real deployments still struggle with three coupled issues: robustness across rooms and bands, efficiency on edge hardware, and unified support for multiple tasks. We present UN-2DCNN, a lightweight 2-D convolutional neural network (CNN) pipeline tailored to indoor, multiuser CSI sensing. The design reduces temporal redundancy via a simple temporal skipping augmentation, learns a compact 128-D representation with a small CNN + global average pooling (GAP) backbone, and injects reliability feedback through uncertainty-aware feature scaling (UAFS): Stage-1 predictive entropy is mapped to a gating weight that rescales features before a second decision head. A channel-attention MLP further suppresses spurious subcarrier responses. Evaluated on a recent multiuser CSI benchmark across classrooms, meeting rooms, and empty environments at 2.4/5 GHz, UN-2DCNN consistently outperforms competitive recurrent neural network (RNN)/Transformer baselines while using only ~1M parameters and maintaining sub-2-s test-time latency. Beyond higher accuracy, the model exhibits faster, smoother convergence and improved calibration (fewer overconfident errors). Ablations confirm that removing attention, UAFS, or the second-stage head yields consistent drops, and simple temporal skipping on the data side complements model-side selectivity. These results indicate that reliability-aware, lightweight designs can deliver practical accuracy–efficiency tradeoffs for CSI-based perception on edge/IoT platforms.

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Integrated Deployment and Resource Allocation in Multi-Layer UAV-Enabled NOMA Wireless Caching Networks

Yin Y., Bouazizi M., Hu B., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 13 （ 7 ） 14898 - 14911 2026年

　概要を見る

Conventional multi-unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs) usually operate in a distributed and non-collaborative manner, where each UAV serves users independently without coordination or relay support. When UAVs move beyond the communication range of ground base stations (BSs), backhaul disruption occurs, leading to high user latency and limited system scalability. To address these issues, we propose a multi-layer UAV-assisted NOMA WCN architecture, where a primary UAV (PUAV) communicates with the BS and cooperates with multiple secondary UAVs (SUAVs). The PUAV not only acts as a control and coordination hub but also serves as a relay for content transmission to SUAVs when necessary. To minimize user transmission latency, we propose a joint iterative algorithm that integrates user clustering, user pairing, power allocation, and UAV deployment. First, we develop an Advanced Balanced K-Means++ (ABKM) algorithm to ensure that each cluster contains a balanced number of users and to reduce the distance between users and their serving SUAVs. Next, we derive the NOMA power allocation factor that minimizes user transmission latency, ensuring efficient resource distribution among all paired users. Furthermore, we analyze the impact of PUAV and SUAV placement on user latency and propose a two-stage particle swarm optimization (PSO)-based algorithm to iteratively optimize the deployment of all UAVs. Finally, the user pairs and power allocation are jointly optimized based on the updated deployment of all UAVs to further reduce user latency. Simulation results show that, compared with a single-layer UAV architecture and benchmark schemes, the proposed multi-layer design with joint optimization achieves lower user latency. Additionally, comparisons with the optimal power allocation search method confirm the validity of the derived NOMA power allocation factor.

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Handover Optimization for UAV-Assisted LEO Satellite Networks Based on IPPO and Three-Sided Matching Theory

Yang F., Li M., Wang K., Si P., Ohtsuki T., Richard Yu F.

IEEE Internet of Things Journal 13 （ 1 ） 152 - 166 2026年

　概要を見る

Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This article proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

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Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks †

Ariyoshi R., Li A., Hasegawa M., Ohtsuki T.

Sensors 25 （ 16 ）  2025年08月

　概要を見る

The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and (Formula presented.) -greedy methods in both transmission success rate and energy efficiency.

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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar †

Rantelinggi P.H., Shi X., Bouazizi M., Ohtsuki T.

Sensors 25 （ 13 ）  2025年07月

　概要を見る

Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. Our approach introduces a frame concatenation strategy that improves data quality before processing through the neural network pipeline. Experimental evaluations on the MARS dataset demonstrate that our model outperforms conventional methods by significantly reducing estimation errors, achieving a mean absolute error (MAE) of 1.77 cm and a root mean squared error (RMSE) of 2.92 cm while maintaining computational efficiency.

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Model-Driven Meta-Learning-Aided Fast Beam Prediction in Millimeter-Wave Communications

Lu W., Jiang X., Cao Y., Ohtsuki T., Bai E.

Electronics Switzerland 14 （ 13 ）  2025年07月

　概要を見る

Beamforming plays a key role in improving the spectrum utilization efficiency of multi-antenna systems. However, we observe that (i) conventional beam prediction solutions suffer from high model training overhead and computational latency and thus cannot adapt quickly to changing wireless environments, and (ii) deep-learning-based beamforming may face the risk of catastrophic oblivion in dynamically changing environments, which can significantly degrade system performance. Inspired by the above challenges, we propose a continuous-learning-inspired beam prediction model for fast beamforming adaptation in dynamic downlink millimeter-wave (mmWave) communications. More specifically, we develop a meta-empirical replay (MER)-based beam prediction model. It combines empirical replay and optimization-based meta-learning. This approach optimizes the trade-offs between transmission and interference in dynamic environments, enabling effective fast beamforming adaptation. Finally, the high-performance gains brought by the proposed model in dynamic communication environments are verified through simulations. The simulation results show that our proposed model not only maintains a high-performance memory for old tasks but also adapts quickly to new tasks.

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Heterogeneous multi-agent deep reinforcement learning based low carbon emission task offloading in mobile edge computing

Zhou X., Guan X., Sun D., Zhang X., Zhang Z., Ohtsuki T.

Computer Communications 234 2025年03月

ISSN  01403664

　概要を見る

Mobile edge computing is an emerging computing paradigm in the Internet of Things. Task offloading is a critical method in mobile edge computing to alleviate computational resource constraints. Nowadays, the rising number of tasks is placing greater demands on computing resources. The increasing consumption of computing resources leads to high carbon emission. Achieving environmentally friendly mobile edge computing while effectively managing low-carbon task offloading poses a significant challenge. Recently, deep reinforcement learning has made certain progress in many research fields. However, there are few deep reinforcement learning methods that consider the carbon emission in task offloading. In this paper, we propose a deep reinforcement learning based low carbon emission task offloading algorithm for minimizing carbon emission in mobile edge computing. Firstly, since different base stations exist in the mobile edge computing environment, we consider the mobile edge computing environment with multiple heterogeneous agents. Secondly, to minimize carbon emission, we consider the carbon intensity of the base station as an optimization factor. We conclude the task offloading strategy to minimize carbon emission, consequently achieving the minimization of carbon emission. Moreover, our proposed algorithm allows user devices to decide their own preference for task offloading. Based on the specific requirements and preferences of user devices, our proposed algorithm can dynamically adjust the weights of delay, energy consumption, and carbon emission, respectively. Experiments indicate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize carbon emission.

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An Ensemble Learning-Based Fault Detection Method for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Liu J., Zhao H., Han P., Gui G., Ohtsuki T., Sari H., Adachi F.

IEEE Transactions on Vehicular Technology 74 （ 3 ） 5114 - 5124 2025年03月

研究論文（学術雑誌）,  ISSN  00189545

　概要を見る

Vehicular ad hoc networks (VANETs) are integral to Intelligent Transportation Systems (ITS), serving as a crucial platform for deploying applications. The overall performance of VANETs is highly dependent on the vehicles, emphasizing the importance of effective fault (vehicle crash or exit) detection methods. Traditional fault detection faces challenges due to the high mobility of vehicles and prevalent software, hardware, and communication link faults, necessitating a robust solution for timely and accurate fault detection. This paper introduces ET-FD, a novel fault detection method based on ensemble learning designed to address the unique challenges in VANETs. ET-FD satisfies stringent accuracy requirements and aims to reduce system overhead significantly. We present experimental results demonstrating the superior performance of ET-FD in terms of fault detection accuracy and false positive rates, alongside comparisons with other classification methods that highlight its reduced training time. This confirms the potential of ET-FD as an efficient and reliable solution for fault detection in VANET environments.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar †

Wang S., Bouazizi M., Yang S., Ohtsuki T.

Sensors 25 （ 3 ）  2025年02月

　概要を見る

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is (Formula presented.) mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is (Formula presented.) mmHg.

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A Comparison Study of Person Identification Using IR Array Sensors and LiDAR

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

Sensors 25 （ 1 ） 271 2025年01月

研究論文（学術雑誌）, 最終著者

　概要を見る

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems.

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Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation

Okunishi T., Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

IEEE Journal of Biomedical and Health Informatics 29 （ 5 ） 3537 - 3548 2025年01月

研究論文（学術雑誌）, 責任著者,  ISSN  21682194

　概要を見る

The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar

S. Wang, M. Bouazizi, Siyuan Yang, and T. Ohtsuki

Sensors 25 （ 3 ） 619 2025年01月

研究論文（学術雑誌）, 最終著者