論文 - 大槻　知明

mmPoint-Attention: A Unified Attention Framework for Human Pose and Activity Recognition From mmWave Radar Point Clouds

Shi X., Bouazizi M., Ohtsuki T.

IEEE Sensors Journal 25 （ 14 ） 26794 - 26805 2025年

ISSN  1530437X

　概要を見る

Millimeter-wave (mmWave) radar-based pose estimation and activity recognition provide robust, precise tracking of human movement, unaffected by lighting or environmental challenges. However, existing human pose estimation methods using point cloud data typically rely on single-frame inputs or apply voxelization, both of which have inherent limitations. Moreover, the conventional methods for activity recognition using point cloud often rely on multistep processes that lose contextual information and depend on pose accuracy. To address these issues, in this paper, we propose mmPoint-Attention, a unified attention and Transformer-based model that directly utilizes multiframe point cloud data for both pose estimation and activity recognition without voxelization. Leveraging temporal context and attention mechanisms, our model achieves accurate 3-D joint estimation and activity classification, with postprocessing to enhance sequence consistency. This approach enables high recognition accuracy and computational efficiency across diverse activities compared with conventional methods.

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Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in Non-Stationary Environments

Cao Y., Lu W., Ohtsuki T., Maghsudi S., Jiang X.Q., Tsimenidis C.C.

IEEE International Conference on Communications    1262 - 1267 2025年

ISSN  15503607

　概要を見る

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance on large-scale paired data for model training and tuning, which limits performance gains and makes beam predictions outdated, especially in multi-user mmWave systems with large antenna arrays, and (ii) meta-learning (ML)-based beamforming solutions are prone to overfitting when trained on a limited number of tasks. To address these issues, we propose a memristorbased meta-learning (M-ML) framework for predicting mmWave beam in real time. The M-ML framework generates optimal initialization parameters during the training phase, providing a strong starting point for adapting to unknown environments during the testing phase. By leveraging memory to store key data, M-ML ensures the predicted beamforming vectors are wellsuited to episodically dynamic channel distributions, even when testing and training environments do not align. Simulation results show that our approach delivers high prediction accuracy in new environments, without relying on large datasets. Moreover, MML enhances the model's generalization ability and adaptability.

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Max-Min Strategy for Handover in LEO Based Non-Terrestrial Networks

Nagase R., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2025年

ISSN  15502252

　概要を見る

In Low-Earth Orbit (LEO)-based Non-Terrestrial Networks (NTN), stable and continuous connectivity through efficient handover (HO) mechanisms should be ensured, especially given the high mobility and dynamic satellite coverage in these networks. Conventional HO optimization approaches, such as the Service Continuity Dynamic Programming (SCDP) strategy, optimize HO but may leave some User Equipments (UEs) experiencing low link rates, reduced service time, or frequent handovers. To address this, we propose the Service Continuity Max-Min (SCMM) strategy, which employs the MaxMin algorithm to reduce the number of UEs that have poor connectivity experiences, using a uniquely defined reward function that incorporates link rate, service availability time, and HO frequency. The simulation results demonstrate that SCMM improves the minimum reward across UEs while maintaining an overall performance comparable to that of the existing SCDP methods. This approach offers a robust solution to improve service continuity and user satisfaction in NTN, making it an effective strategy for next-generation satellite networks.

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Malware Traffic Classification via Expandable Class Incremental Learning With Architecture Search

Zhang X., Wang Y., Ohtsuki T., Gui G., Yuen C., Di Renzo M., Sari H.

IEEE Transactions on Information Forensics and Security 20   6074 - 6085 2025年

ISSN  15566013

　概要を見る

Malware traffic classification (MTC) is a crucial step in network intrusion detection, which is significant for network security and management. With the continuous evolution of malware traffic, traditional MTC methods are difficult to adapt efficiently to new traffic categories, and manually designed neural network structures suffer from performance bottlenecks and low design efficiency. Hence, we propose an enhanced MTC method based on expandable class incremental learning (CIL) with architecture search. The architecture search can automatically design the optimal neural network structure tailored to different network traffic characteristics, avoiding the limitations of manually designing network structures and improving classification performance. Meanwhile, expandable CIL allows the MTC model to gradually learn new traffic categories without forgetting previous knowledge, avoiding the computational overhead and efficiency loss caused by frequent retraining of the model. The experimental results demonstrate that the proposed CIL-MTC approach surpasses advanced incremental learning methods on both the Edge-IIoTset and ISCX VPN-nonVPN datasets, achieving superior classification performance while maintaining lower average trainable parameters and training costs. Especially, it achieves an average incremental accuracy of 98.55% and 99.09% on the Edge-IIoTset dataset with incremental tasks of 5 and 2, respectively.

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Lightweight CSI-Based Human Activity Recognition for Multitask IoT Applications

Liu C., Miao F., Chen Y., Wu J., Wan H., Tang T., Ohtsuki T., Gui G., Sari H.

IEEE Internet of Things Journal 12 （ 17 ） 35817 - 35827 2025年

　概要を見る

As the global population continues to age and technologies, such as the Internet of Things (IoT) and edge computing advance rapidly, indoor human activity recognition (HAR)-based on Wi-Fi channel state information (CSI) has gained significant research attention. However, the high computational complexity of existing HAR methods limits their deployment on resource-constrained devices. To address this challenge, we propose a lightweight HAR method using branch decision lightweight two-stream convolution-augmented transformer (BLTHAT) model, which integrates depthwise separable convolutions (DSC) and an improved framework structure to enhance computational efficiency. Additionally, we introduce the branch fusion network (BFN), a decision-making module designed to optimize feature processing and improve model robustness. Further enhancements in attention mechanisms and regularization strategies contribute to reducing complexity while maintaining high recognition accuracy. Comprehensive experiments were conducted on a multilabel dataset. The results demonstrate that our proposed HAR method achieves high computational efficiency with minimal complexity, making it well-suited for IoT applications. Ablation studies further confirm that the multibranch structure of the BFN module enhances feature extraction without significantly increasing computational overhead.

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Large Model Empowered Task Offloading for Multi- Access Edge Computing in the Internet of Vehicles

Zhou X., Guan X., Wang N., Chen H., Ohtsuki T., Zhang Y., Han Z.

IEEE Transactions on Vehicular Technology 74 （ 11 ） 18012 - 18025 2025年

ISSN  00189545

　概要を見る

The rapid advancement of the Internet of Vehicles (IoV) has created significant challenges in large-scale data processing and low-latency decision-making. Multi-access edge computing substantially enhances data processing capabilities and system response times by pushing computational resources to the network edge. Task offloading technology transfers vehicle computing tasks to edge servers, alleviating the computational burden on user devices. Traditional offloading methods struggle with the challenges posed by the rapid growth of IoV. With the advancement of deep learning, large models have emerged as powerful tools for handling complex tasks. In this paper, we propose a task offloading algorithm that integrates a Transformer-based large model with deep reinforcement learning. We utilize the Transformer-based large model to replace the action generation network in deep reinforcement learning. The proposed method utilizes extensive historical data and real-time task information to dynamically adjust task allocation schemes. This approach enables the conclusion of task offloading strategies that minimize energy consumption and latency. Additionally, we propose a task classification framework to further reduce offloading delays and energy consumption. The framework categorizes tasks based on key attributes such as computational complexity, data size, and time sensitivity. We propose a reward function based on task adaptability to conclude the task offloading strategy that minimizes both offloading delay and energy consumption. Experimental results demonstrate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize energy consumption and latency.

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Introduction to Activities of AI and Digital Twin WG in XGMF 6G Radio Technology Project

Yamamoto T., Yamazaki T., Otsuki T., Suyama S., Liu L., Osawa N., Yamamoto H., Watanabe T., Kuriyama K., Koshimizu T., Tsukamoto Y., Tomeba H., Nagao T., Chang Y., Morita K., Kitano H.

2025 IEEE Vts Asia Pacific Wireless Communications Symposium Apwcs 2025 2025年

　概要を見る

This paper introduces activities of AI and Digital Twin working group (AI-DT WG) in XGMF 6G Radio Technology Project. Especially in the fiscal year 2024, AI-DT WG has been engaged in activities for getting the beyond 5G white paper "AI/ML and Digital Twin Technologies"ready for the world. The AI-DT WG also launched the discussion for establishing a shared dataset for AI training. This paper provides an overview of the white paper and the status of the discussion for shared dataset establishment.

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GA-CBLN: A Generative Augmented Cascade Broad Learning Network for Efficient Spectrum Prediction with Missing Data Handling

Ji N., Feng Z., Wang S., Zhang Y., Otsuki T., Gui G., Sari H.

2025 IEEE International Symposium on Dynamic Spectrum Access Networks Dyspan 2025 2025年

　概要を見る

With the advancements in wireless communication and the growth of Internet of Things (IoT), spectrum scarcity has become a key bottleneck. Spectrum prediction enhances resource utilization and supports dynamic spectrum access (DSA). However, current models struggle with missing data and prioritize accuracy over efficiency, limiting practical use. To address this, we propose a generative augmented cascade broad learning network (GA-CBLN) for spectrum prediction, combining a deep learning (DL) pre-trained model with an online broad learning (BL) process. First, a DL-based regressor fills missing values by learning spatiotemporal patterns. Next, a generative adversarial network (GAN) generates synthetic samples to enrich the training set, improving adaptability. Finally, the cascade BL network (CBLN) uses the combined data for efficient prediction. Simulations on real datasets show that GA-CBLN significantly outperforms traditional methods in accuracy and efficiency, even with high missing data rates.

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Enhancing Dementia Detection: Augmenting the Face Mesh

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

Proceedings International Symposium on Biomedical Imaging 2025年

ISSN  19457928

　概要を見る

Alzheimer's disease (AD) stands as a prevalent form of dementia, primarily impacting the elderly, and presently lacking any known cure. Early screening and intervention have demonstrated efficacy for both current and prospective treatments. With the global population aging, early detection of AD has become imperative. Language-based approaches for dementia detection have received considerable attention in research due to dementia's nature of hurting cognitive and language functions. Facial features, on the other hand, have been comparatively neglected despite their potential in identifying AD, due to a lack of usable dataset. AD patients show different patterns while displaying facial expressions because of Apathy, a phenomenon common in AD patients. This makes facial features non-negligible in dementia detection. The face mesh, comprising 478 landmarks, serves as a rich and informative facial representation. This study concentrates on leveraging face mesh data for AD detection. We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. By augmenting the dataset by rotation, shift, and scaling, we improved the performance of the deep learning methods. The combined use of 1D CNN networks and data augmentation method reported the highest accuracy of 76%. These results confirmed the effectiveness of our data augmentation methods. They also underscore the potential of face-related features in enhancing early detection approaches for AD.

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Enhancing Cross-Domain Robustness in Wi-Fi-Based Human Activity Recognition via Attention-Driven Deep Learning

Miao F., Takyu O., Shan L., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference 2025年

ISSN  10903038

　概要を見る

Wi-Fi-based Human Activity Recognition (HAR) using Channel State Information (CSI) has received significant attention due to its non-intrusive nature and wide applicability in smart cities, healthcare, and smart home systems. Despite its potential, real-world deployment faces a major challenge: ensuring the model's ability to generalize across different users, who exhibit distinct behaviors and encounter diverse environmental factors. This issue, termed Cross-User Generalization (CUG), arises from distribution shifts caused by variations in users' body shapes, postures, movements, and environmental conditions. To address this challenge, we propose a cross-domain generalization framework based on an Attention-based Bidirectional Long Short-Term Memory (ABLSTM) network. The proposed framework enhances the generalization capabilities of HAR models by leveraging multi-source training and incorporating attention mechanisms for effective temporal feature learning. Specifically, the model learns user-invariant features across diverse training users and adapts to new users without requiring direct exposure to their data. Extensive experiments on a multi-user CSI dataset demonstrate that the ABLSTM model improves average test accuracy by 2.0%-6.7% compared to BiLSTM and GRU models, and over 18.0% compared to MLP-based models, while achieving the lowest training and testing loss. This methodology offers a robust and scalable solution for Wi-Fi-based HAR, promoting reliable deployment in complex, multi-user environments encountered in smart cities, healthcare monitoring, and security surveillance.

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

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

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

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Efficient WiFi Device Recognition via Blueprint Separable Residual Network with SE Module

Liu T., Cai Z., Wang Q., Ohtsuki T., Sari H., Gui G.

IEEE Vehicular Technology Conference 2025年

ISSN  10903038

　概要を見る

With the rapid advancement of wireless communication technologies, WiFi signals have become essential for modern connectivity across diverse applications. However, their widespread deployment introduces significant security vulnerabilities, including unauthorized access, data leakage, and interference. Accurate identification of WiFi transmitters is crucial for mitigating these threats. While existing methods perform well in ideal conditions, their effectiveness degrades in real-world scenarios, particularly in environments with low signal-to-noise ratios (SNRs). To address this limitation, we propose a novel transmitter identification framework that integrates blueprint separable convolution (BSC) and a squeeze-and-excitation (SE) module. The BSC extracts critical features efficiently, while the SE module dynamically enhances feature representations. Simulation results demonstrate that the proposed approach achieves competitive or superior accuracy compared to state-of-the-art models. Moreover, the framework exhibits strong robustness, maintaining high recognition performance even in challenging transmission conditions with low SNRs.

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Efficient and Effective Fine-tuning Method for Depression Detection from Conversation

Ikeuchi K., Kishimoto T., Nakai F., Horigome T., Kitazawa M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Depression has a significant impact on society. Early detection of depression is essential because timely treatment can mitigate its negative impact. Recent studies have reported that models based on Large Language Models (LLMs), such as Chat GPT-4, can achieve high accuracy in detecting depression when prompt-engineering techniques are applied. In contrast, fine-tuning methods require more memory for training and have yet to demonstrate comparable accuracy to prompting-engineering in detecting depression from conversations. The objective of this study is to achieve higher accuracy than prompt-engineered models with low GPU memory through fine-tuning. To accomplish this, we first quantized the parameters of the LLM to minimize memory requirements during training. Instead of adding a classification layer, we retained the original structure of the LLM and trained it as a generative model. Our evaluation on the DAIC-WOZ dataset demonstrated an F1 score of 84% for depression detection with low GPU memory, which is superior to the performance of prompt-engineered models. Additionally, when tested on the private PROMPT dataset, the proposed model achieved an F1 score of 82%. This result suggests that the model's strong performance is not solely dependent on publicly available data that may have been included in the pre-training.

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Distributed Gossip-GAN for Low-Overhead CSI Feedback Training in FDD mMIMO-OFDM Systems

Cao Y., Liu G., Ohtsuki T., Yang H.H., Quek T.Q.S.

IEEE Internet of Things Journal 12 （ 19 ） 40176 - 40189 2025年

　概要を見る

The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive Multiple-Input Multiple-Output (mMIMO) systems. However, these DAE approaches presented in prior works rely heavily on large-scale data collected through the base station (BS) for model training, thus rendering excessive bandwidth usage and data privacy issues, particularly for mMIMO systems. When considering users’ mobility and encountering new channel environments, the existing CSI feedback models may often need to be retrained. Returning back to previous environments, however, will make these models perform poorly and face the risk of catastrophic forgetting. To solve the above challenging problems, we propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework. Notably, Gossip-GAN enables the CSI feedback training with low-overhead while preserving users’ privacy. Specially, each user collects a small amount of data to train a GAN model. Meanwhile, a fully distributed gossip learning strategy is exploited to avoid model overfitting, and to accelerate the model training as well. Simulation results demonstrate that Gossip-GAN can: 1) achieve a similar CSI feedback accuracy as centralized training with real-world datasets; 2) address catastrophic forgetting challenges in mobile scenarios, and 3) greatly reduce the uplink bandwidth usage. Besides, our results show that the proposed approach possesses an inherent robustness.

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DFusion-SLAM: A Lightweight Semantic Fusion Framework for Robust Visual SLAM in Dynamic Environments

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

IEEE Internet of Things Journal 12 （ 24 ） 55682 - 55692 2025年

　概要を見る

In dynamic and cluttered environments, traditional simultaneous localization and mapping (SLAM) systems often suffer from degraded localization accuracy and unstable map construction due to the presence of moving objects and occlusions. To address these challenges, we propose DFusion-SLAM, a lightweight and robust SLAM framework that integrates an enhanced object detection module into ORB-SLAM3. The detection module is based on an improved D-Fine architecture, in which the original Transformer is replaced with a more efficient PolaLinearAttention mechanism. Furthermore, a MetaFormer-based semantic fusion structure is introduced to strengthen multiscale feature representation. These architectural improvements jointly enhance detection accuracy while reducing model complexity, achieving a performance increase from 42.8% to 43.7% mean average precision (mAP). Experimental evaluations on dynamic RGB-D sequences from the TUM and Bonn datasets demonstrate that DFusion-SLAM significantly improves localization accuracy and mapping stability under dynamic conditions, while maintaining high computational efficiency. These results highlight the framework's strong potential for real-time deployment in IoT-oriented mobile and robotic platforms operating in complex environments.

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Deep Learning-Based Human Joint Localization Using mmWave Radar and Sequential Frame Fusion

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

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Unlike conventional techniques relying typically on RGB and depth cameras, millimeter wave (mmWave) radars, especially Frequency-Modulated Continuous Wave (FMCW) radar, have significant advantages in human monitoring. These advantages are more pronounced when detecting human joint positions, as FMCW radars rely solely on collected point cloud data. Compared with camera-based systems, this technology is efficient because it can operate in low-light conditions, and does not present a privacy threat. However, conventional approaches employing FMCW radar, such as voxel-based gathering techniques, still have limitations in optimally capturing spatiotemporal features, making it difficult to detect human joint points with high accuracy. To overcome this obstacle, we propose a novel framework that introduces sequential concatenation of multiple frames in the preprocessing stage to maintain spatiotemporal continuity, thereby improving feature representation. Furthermore, we design a deep learning architecture consisting of Convolutional Neural Network (CNN) for spatial feature extraction, Transformer for capturing long-term dependencies, and Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal sequence modeling. The proposed approach makes optimal use of spatiotemporal data, improves the accuracy and reliability in detecting human joint positions, and accelerates model convergence. Through experimental validation, this method shows a Mean Absolute Error (MAE) of 1.77 cm and a Root Mean Squared Error (RMSE) of 2.92 cm between the actual human joint positions and the predicted ones. This reaffirms the effectiveness of the model in estimating human joint positions.

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Correlation Analysis of Objective Features and Online Meeting Quality

Oba R., Zheng C., Akahori W., Yamada M., Yokoyama M., Ishii M., Ohtsuki T.

Lecture Notes in Computer Science 15805 LNCS   172 - 183 2025年

ISSN  03029743

　概要を見る

Meetings are vital for team activities, supporting both decision-making and information sharing, making their quality a critical factor in boosting productivity and reducing costs. Therefore, establishing objective methods for measuring meeting quality is essential to identify specific issues and verify the effectiveness of solutions. However, no well-defined objective features have yet been identified to accurately assess meeting quality. In this study, we explored features that can be extracted from meeting video and audio data to objectively assess the quality of meetings. We conducted online meetings and extracted features from video data, including Action Units, Mel-Frequency Cepstral Coefficients (MFCC) and utterances. We then examined the correlations between these features and participant responses regarding psychological safety and perceived meeting effectiveness. As a result, we found that certain features such as the number of utterances and the Action Units such as Lip Part and Dimpler showed significant correlations with psychological safety and perceived meeting effectiveness.

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User Clustering and Trajectory Optimization for UAV-Assisted Wireless Sensor Network

Zhang X., Yin Y., Ohtsuki T.

2025 30th Asia Pacific Conference on Communications Apcc 2025 2025年

　概要を見る

With the advances in flexibility, wide coverage, and cost-effectiveness, integrating unmanned aerial vehicles (UAVs) with wireless sensor networks (WSNs) has been regarded as a promising technology in data collection and transmission within complex environments. Due to the limited battery capacity of sensor nodes (SNs) and UAVs, UAV-assisted WSNs face challenges in achieving energy efficiency and optimizing resource allocation. To reduce the total energy consumption of UAV-assisted WSNs, we design a multifactor fitness function which includes the distance between SNs, energy levels of SNs, and UAV trajectory length. Based on that, we propose a balanced clustering and cluster head (CH) selection strategy. As for UAV trajectory design, we employ the branch and bound method according to the selection of CHs. Simulation results show that the proposed algorithm reduces the average energy consumption of all SNs and achieves short UAV trajectory length compared to Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed clustering (HEED), Particle Swarm Optimization (PSO), and Stable Election Protocol (SEP).

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Model-Based DRL for Dynamic Antenna Beamforming in HAPS Communication System

Yang S., Bouazizi M., Ohtsuki T.

2025 International Symposium on Antennas and Propagation Isap 2025 2025年

　概要を見る

High Altitude Platform Station (HAPS) systems require dynamic beamforming to handle platform mobility and uneven user distributions. However, traditional reinforcement learning (RL) and evolutionary algorithms run the risk of degrading throughput during exploration. We propose a model-based deep RL (DRL) framework that pre-trains an environment model to map antenna parameters and HAPS states to SINR distributions. This simulated feedback allows efficient parameter exploration without reducing average throughput below its initial level. Simulation results show that our method consistently maintains higher throughput than conventional RL approaches and swiftly adapts to network changes.

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Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar

Nakatani S., Bouazizi M., Ohtsuki T.

Sensors 24 （ 21 ）  2024年11月

　概要を見る

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar’s coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

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