研究者詳細 - 大槻　知明

論文 - 大槻　知明

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Mobile Edge Computing Based Intelligent Charging Strategy for Electric Vehicles in Cyber Physical Energy System

Pan G., Guan X., Wang N., Liu Y., Wu H., Chen H., Ohtsuki T., Han Z.

IEEE Transactions on Vehicular Technology 75 （ 2 ） 3205 - 3221 2025年

責任著者, ISSN 00189545

The development of intelligent transportation systems has promoted the application of wireless technology in vehicular communication networks and mobile services. At the same time, electric vehicles are popular due to their environmental-friendliness and cost-saving characteristics. However, charging of electric vehicles without proper and real-time rules may cause serious traffic congestion and high expense, which results in unnecessary charging stations and wasted power generation with high carbon emission. Therefore, it is crucial to design an efficient charging strategy for electric vehicles without causing road congestion, together with an economic dispatch strategy to offer proper power generation and decarbonization. In this paper, we introduce mobile edge computing architecture to enhance real-time response capabilities through edge device positioning data. This approach allows charging strategies to swiftly adapt to varying road conditions and user demands, effectively mitigating traffic congestion. Regarding the forecasting of load demand for charging stations, this paper employs a deep learning model based on informer and deploys it on edge servers, quickly providing accurate demand predictions for economic dispatching and enhancing the precision of scheduling strategies. With the application of deep reinforcement learning models, the system can formulate efficient charging plans based on real-time user data, improving user satisfaction and quality of services. Numerical results show the efficiency of the obtained strategies for electric vehicles charging and power economic dispatching, and the two-stage modeling approach significantly improves the convergence of the model and the quality of the solution.

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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

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

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

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

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

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

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

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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Channel-Robust Few-Shot Specific Emitter Identification Using Meta-Feature Augmentation

Fu X., Meneghello F., Wang Y., Ohtsuki T., Yuen C., Gui G., Sari H.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN 15253511

The rapid increase in wireless devices has raised significant security and privacy concerns, positioning Specific Emitter Identification (SEI) as a crucial physical-layer security technology. While Deep Learning (DL) has been widely applied to SEI, it often requires large amounts of high-quality signal examples, which are laborious and expensive to obtain. Moreover, the DL-enabled SEI models have difficulties in extracting features from the signal examples in the testing process that are consistent with those from the signal examples in the training phase due to the wireless channel variations, further resulting in a significant reduction in identification performance. To address these challenges, we propose a channel-robust Few-Shot SEI (FS-SEI) method based on Meta-Feature Augmentation (MFA). Our approach utilizes datasets from base emitters to construct a meta-feature embedding function that can extract generalizable features from a few signal examples of target emitters. We then calculate and calibrate the statistics of these extracted features to describe the feature distribution of target emitters. A Multi-Layer Perceptron (MLP) is subsequently trained on both original and augmented features derived from this distribution, achieving a robust FS-SEI model. Experiments conducted on a Wi-Fi dataset comprising 16 emitter categories - 10 as base emitters and 6 as target emitters - demonstrate that our method achieves 93.75% identification accuracy with only 5 examples per target emitter, maintaining 92.56% accuracy even under varying wireless channel conditions. Code is available at https://github.com/lovelymimola/MFA-based-FS-SEI.

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Beamforming and Trajectory Planning Method Under Fixed-Footprint Conditions for Multi-HAPS Systems

Mori T., Ohtsuki T., Pan M., Han Z.

IEEE International Conference on Communications 2236 - 2241 2025年

ISSN 15503607

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, 'footprint fixation,' which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a 20 - 36% improvement in 5th percentile throughput and an 8-20% improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2% and significantly reducing handover frequency.

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A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout

Hou Z., Ohtsuki T.

IEEE International Conference on Communications 4553 - 4558 2025年

ISSN 15503607

Deploying deep neural network (DNN) models to the Internet of Things (IoT) for distributed inference (DI) is becoming an increasingly common requirement for AI applications. When IoT transmissions occur over lossy networks, data loss can significantly affect the accuracy of inference tasks. Therefore, we propose a plug-and-play module called Loss-Adapter, which can improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This idea is utilized during the training of Loss-Adapter to acquire fault-tolerant inference capability on unreliable communication links. Moreover, we use the Tree-structured Parzen Estimator algorithm to optimize the parameters of the Gaussian dropout layer. To avoiding catastrophic forgetting, we implement freeze training. In addition, as a plug-and-play module, it is very easy to train and use. Our experiments show that by applying the Loss-Adapter, the inference accuracy of DNN models on lossy IoT is significantly improved.

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A Novel Physical Spoofing Technique Using Radio Frequency Fingerprint Emulation and Model Fitting

Yao Z., Wang Y., Gui G., Otsuki T., Mao S., Wang X., Sari H.

IEEE International Conference on Communications 2629 - 2634 2025年

ISSN 15503607

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

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A Novel MIMO FMCW Radar-Based Approach for Heart Rate Estimation Using Positional Feature Selection

Nakatani S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications 916 - 921 2025年

ISSN 15503607

This paper proposes a heart rate estimation method using Variational Mode Decomposition (VMD) employing Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The proposed method first estimates the human position within the radar's coverage area, then reduces noise by focusing on the signal from these positions. The signal is decomposed into Intrinsic Mode Function (IMF) signals using VMD, and only the IMF signal related to the heartbeat is retained. Heart rate signal is reconstructed by weighting IMF signals based on their energy within the specific spatial area in which the human is located. The reconstructed signals are utilized for heart rate estimation through peak detection. The estimation is done over consecutive time windows. Until the fourth time window, among the estimated heart rates from the different cells, the selection is based on energy and periodicity. From the fifth time window onwards, the heart rate closest to the average of the previous four estimates is chosen. This approach considers the gradual transition of heart rate estimation over time to mitigate extraneous variations. Validation experiments involved 4 subjects being seated and stationary within the radar coverage area, with heart rate observed via MIMO FMCW radar. Results showed an average Mean Absolute Error (MAE) of 2.54 BPM, with an exclusion rate of 2.12%.

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A Deep Transfer Learning-Based Low-Overhead Beam Prediction in Vehicle Communications

Xiao Z., Cao Y., Bouazizi M., Ohtsuki T., Mumtaz S.

IEEE Wireless Communications Letters 14 （ 12 ） 4147 - 4151 2025年

ISSN 21622337

Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model’s performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model extracts domain-invariant features in adversarial training with domain classifier, which can enhance model performance in the target domain. Simulation results demonstrate that the proposed transfer learning-based beam prediction method achieves better achievable rate performance than the pure fine-tuning method in the target domain, and close to those when the training is done from scratch on the target domain.

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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

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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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

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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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

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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Towards Robust and Real-Time State Estimation: A Neural Kalman Filter Framework with Mamba Architecture

Sun J., Shen J., Tong J., Tang T., Yin Y., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2025年

ISSN 00189545

The rapid advancement of intelligent perception systems and autonomous navigation technologies imposes increasingly stringent requirements on state estimation algorithms regarding modeling capacity, robustness, and real-time performance. While traditional Kalman Filter (KF) demonstrates computational efficiency and mathematical interpretability for linear Gaussian systems, their effectiveness diminishes in nonlinear, non-Gaussian, and non-stationary dynamic scenarios. Recent developments in neural state space modeling offer new perspectives for recursive estimation, yet existing approaches suffer from structural complexity, training difficulties, and inadequate real-time capability. This paper presents Mamba-KF, a novel state estimation framework integrating deep modeling with recursive filtering. Leveraging the cutting-edge Mamba state space neural network, our method dynamically generates state transition parameters while establishing a differentiable covariance propagation pathway, enabling end-to-end optimization. We further design a lightweight multi-modal fusion architecture tailored for high-frequency sensory data processing in real-time applications. Evaluated on the EuRoC Micro Aerial Vehicle (MAV) dataset, Mamba-KF attains the lowest errors across states, reducing position Root Mean Square Error (RMSE) by up to 33.3% relative to Extended Kalman Filter (EKF) and 24.1% relative to Unscented Kalman Filter (UKF) at 10 dB Signal-to-Noise Ratio (SNR), with an average velocity RMSE of 0.097 m/s and an orientation error of 2.3°. It also achieves superior consistency, with a mean Normalized Estimation Error Squared (NEES) of 6.02 and 93.7% of estimates within the χ<sup>2</sup>-based 95% confidence interval. Under robustness evaluations, Mamba-KF yields the lowest RMSEs and suppresses divergence observed in EKF. Computationally, it maintains near real-time performance with 0.65 ms per step, offering a favorable accuracy-efficiency trade-off for visual-inertial navigation and autonomous systems, while providing significant theoretical insights and promising engineering applications.

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Stress Detection Using Eye Activity via Recurrent Neural Networks

Bouazizi M., Feghoul R., Feghoul K., Ohtsuki T.

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

ISSN 1557170X

Chronic stress has become a significant issue in modern society, negatively affecting individuals' overall well-being and potentially leading to severe physiological and psychological disorders. Most existing methods for automatic stress detection rely on physiological data, which requires direct skin contact with sensors. Not only does this make them intrusive, but it also limits their practicality for everyday use due to accuracy constraints. In this paper, we propose a novel approach using eye data captured by a standard video camera -specifically, eye gaze direction and eye landmarks- for stress detection. We use two types of Recurrent Neural Networks (RNNs), namely a Long Short-Term Memory (LSTM) network and a Gated Recurrent Unit (GRU) network. Through experiments on the UBFC-Phys dataset, we show that our method achieves a stress detection accuracy of up to 83.69% using gaze data, while eye landmarks further improve accuracy to 90.12%. Combined, these two modalities reach accuracy of 90.83%. These results highlight the effectiveness of our approach in enabling accurate and non-intrusive stress detection.

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Stress Detection Through Eye Tracking Incorporating Custom Lasso Stress Index

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications 3174 - 3179 2025年

ISSN 15503607

Stress has become an increasingly common issue, significantly impacting individuals' health and productivity. Traditional methods for assessing stress levels often rely on physiological signals, such as heart rate and skin conductance. However, these methods can be impractical for continuous, non-invasive monitoring. To address these limitations, we propose a machine learning approach for stress assessment using eye-tracking data. We collected eye-tracking and Electrocardiogram (ECG) data under different stress conditions. From this dataset, we extracted relevant features and conducted preliminary analysis. We further proposed a novel stress index, the Least Absolute Shrinkage and Selection Operator Stress Index (Lasso Stress Index), specifically tailored for stress detection in our dataset. In previous studies, researchers commonly used predefined subjective stress index or Baevsky's Stress Index (BSI) for stress classification. We compared these methods and found that our proposed Lasso Stress Index yielded the highest accuracy, achieving up to 90.6% in stress classification.

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