論文 - 大槻　知明

A Low-Complexity Clustering-Aided DQN Method for Dynamic Antenna Control in HAPS

Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.

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A CSI-based Cross-subject Transfer Learning Method Using Network Freezing

He Z., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3515 - 3520 2024年

ISSN  23340983

　概要を見る

Currently, the field of wireless sensing is moving towards non-contact and easy-to-deploy passive sensing technologies. Among these, sensing techniques based on WiFi channel state information (CSI) have emerged as highly promising due to their excellent performance and suitability for current sensing needs. However, despite the potential of WiFi CSI-based sensing technologies, there are still pressing issues that need to be addressed. Traditional WiFi CSI-based methods face challenges such as weak generalization ability and poor robustness, especially when the sensing target and environment change, which hinders the further development. To address this problem, this paper introduces the transfer learning method and combines it with the deep neural network to propose a novel cross-subject WiFi sensing method. Specifically, this method utilizes the source domain-target domain partitioning approach in transfer learning. In the source domain, deep neural networks are trained to obtain the source domain feature model, which is then transferred to the target domain. Fine-tuning is performed using techniques such as network freezing, aiming to meet the faster and more accurate sensing task demands of cross-subject. From the final experimental results, the novel cross-subject transfer learning method proposed in this paper achieved higher recognition accuracy and shorter training time. Moreover, the implementation of network freezing further enhanced the performance and efficiency of cross-subject, achieving up to 86% performance on the dataset used in this paper.

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A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

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

IEEE Access 12   152196 - 152211 2024年

　概要を見る

In recent years, non-contact biometric identification has garnered significant attention owing to its flexibility and its capacity to ensure privacy and confidentiality. Previous research has predominantly centered on harnessing cardiac radar signals detected by radar systems. This paper aims to compare two methods for person identification utilizing heartbeat signals extracted from radar data. In the first approach, we directly employ the heartbeat time series data as input for a deep learning model called InceptionTime dedicated to person identification. The second method explores the viability of using spectrograms, which are images generated from cardiac radar heartbeat signals, combined with other deep learning models, ResNet and a small convolutional neural network (CNN) model built by us, to achieve the same goal. Additionally, we assessed the robustness of both methods by introducing white Gaussian noise into the signals and evaluating their performance under different noise levels. For the second method, we implemented three image denoising methods: autoencoder, deep image prior (DIP), and Dilated-Residual U-Net (DRU-Net) to denoise the spectrograms after adding noise in the time domain. Experimental results reveal that the accuracy of person identification using spectrograms is 100% without adding noise based on the public dataset. The identification accuracy is the same as that using time series data. Following the introduction of noise, the accuracy of both methods decreases; however, the method based on spectrograms exhibits a more pronounced decline in accuracy compared to the approach using time series data directly especially for the small CNN model we built. The image-based method with DIP image denoiser shows outstanding performance when the value of noise is high compared with InceptionTime.

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3D-LiDAR-Based Fall Detection by Dynamic Non-Linear Mapping with LSTM

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    818 - 823 2024年

ISSN  15503607

　概要を見る

With the aging of the population, the health of the elderly people has become a concern for many families. Monitoring of elderly people and the detection of their activities have become hot research topics. Several approaches have been proposed in the literature to detect such activity (e.g., falls), but their reliance on RGB cameras poses a serious threat to the privacy of the elderly. Therefore, Light Detection and Ranging (LiDAR) has attracted the attention of researchers. In this paper, we propose a novel method for fall detection using transfer learning and dynamic non-linear mapping to extract the human skeletons from depth images and implement a fall detection method using a Long Short-Term Memory (LSTM) neural network. We collected RGB images and depth images using 3D-LiDAR. The RGB images are used as a ground-truth for annotation and evaluation, whereas depth images are used as input to our proposed method. For fall detection, we achieved 98.5% accuracy, 97.0% precision, 100% recall, and 98.5% F1 score.

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Low Complexity CSI Feedback Method Using Reformer

Saito Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

To leverage antenna diversity effectively in Massive Multiple-Input Multiple-Output (MIMO) systems, Channel State Information (CSI) utilization at the base station (BS) is crucial. In Frequency Division Duplexing (FDD) MIMO setups, downlink CSI is acquired by user equipment (UE) and transmitted back to the BS. However, this feedback overhead diminishes communication throughput, necessitating CSI compression technologies. Methods such as traditional compression or more advanced ones such as Transformer-based neural networks which aim to address this issue suffer from low compression rates or high computational cost. In this context, Reformers emerge as a potential solution as they employ Locality-Sensitive Hashing (LSH) attention, offering a comparable accuracy to Transformers but with reduced computational demands. In this paper, we propose a CSI feedback method using Reformer. Specifically, by replacing the multi-head attention of the CSI feedback method using Transformer with the LSH attention of Reformer, we aimed to achieve similar CSI reconstruction accuracy while reducing computational complexity. Computer simulations confirm that the CSI feedback method using Reformer reduces computational complexity by about 12% while achieving comparable CSI reconstruction accuracy compared to the CSI feedback method using Transformer.

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Toward Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy

Li Z., Bouazizi M., Ohtsuki T., Ishii M., Nakahara E.

IEEE Access 12   11010 - 11023 2024年

　概要を見る

Local model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models. In the industry, practitioners focus not only on how these explanations can validate their models but also on how they can help maintain trust from end-users. Some studies attempted to measure this ability by quantifying what they refer to as the explainability or interpretability of ML models. In this paper, we introduce a new method for measuring explainability with reference to an approximated human model. We develop a human-friendly interface to strategically collect human decision-making and translate it into a set of logical rules and intuitions, or simply annotations. These annotations are then compared with the local explanations derived from common XAI tools. Through a human survey, we demonstrate that it is possible to quantify human intuition and empirically compare it to a given explanation, enabling a practical quantification of explainability. By relying on this new method, we identified several potential flaws in today's ML selection process. Furthermore, we demonstrate how our method can help to better evaluate ML models.

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Source CSI Dataset for Multi-Task CSI Feedback

Inoue M., Ohtsuki T.

Proceedings IEEE Consumer Communications and Networking Conference Ccnc    1042 - 1043 2024年

ISSN  23319860

　概要を見る

The muti-task Channel State Information (CSI) feedback is a downlink CSI feedback approach. It focuses on Clustered Delay Line (CDL) channel models as the channel environments. There are five different CDL channel models, from CDL-A to CDL-E. In this method, the autoencoder (source model) is pre-trained using the CDL-ALL dataset, which is an equal mixture of each CDL channel dataset (source data) rather than a single CDL channel dataset. The decoder at the BS is subsequently fine-tuned using a small amount of CSI data from the target channel environment (target data) to generate a target model. The source model has a significant influence on the reconstruction performance of the target model, the amount of data, and the number of training epochs required to obtain it. This paper investigates the mixing ratio of each CDL channel dataset used as source data to enhance the CSI reconstruction performance of the source model in the muti-task CSI feedback. Furthermore, we explore the possibility of enhancing the CSI reconstruction performance of the target model by employing the improved source model for fine-tuning the target model. To determine the mixing ratio, two distinct criteria for source model performance are employed. The simulation results identified several mixing ratios of source data in these criteria that improve the reconstruction performance of both the source and the target models compared to the use of CDL-ALL as a source dataset in the multi-task CSI feedback.

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Rough-to-Fine Model-based Non-contact Heart Rate Estimation using MIMO FMCW Radar

Shi X., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    920 - 925 2024年

ISSN  23340983

　概要を見る

MIMO FMCW Radar, a non-contact heart rate (HR) monitoring technology, has emerged as one of the superior alternatives to contact-based sensors, providing precise HR estimation without the drawbacks of discomfort or privacy concerns, and excelling in diverse environmental conditions. Recent HR estimation studies using conventional methods have achieved high accuracy but face challenges with lengthy processing times and sensitivity to experimental conditions, affecting real-time application and robustness. A previous Deep learning (DL)-based approach improves on these aspects but is limited by the range resolution of SISO FMCW Radar and the requirement for close positioning of subjects. Additionally, this method has inability to utilize contextual information from adjacent time windows restricts its effectiveness for time series analysis. Thus, our proposed method combines a Curve-Length (CL) approach with a DL rough-to-fine model, addressing the limitations of previous studies and improving HR estimation accuracy and robustness. This integrated process begins with human location detection through a CL-based approach and is followed by a two-phase rough-to-fine HR estimation. The experimental results show significant improvement over the conventional methods.

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Remote Inter-beat Interval Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

Wang S., Bouazizi M., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.

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Object Detection on SAR Images Via YOLOv10 and Integrated ACmix Attention Mechanism

Wang Z., Miao F., Huang Y., Lu Z., Ohtsuki T., Gui G.

2024 6th International Conference on Robotics Intelligent Control and Artificial Intelligence Ricai 2024    756 - 760 2024年

　概要を見る

With the rapid advancement of deep learning techniques, object detection on Synthetic Aperture Radar (SAR) images has shown increasing potential in recent years. However, due to the unique microwave imaging mechanism of SAR, targets can easily be confused with background structures like harbors and aircraft aprons, leading to poor recognition accuracy. In this paper, we propose YOLOv10n-ACmix, an enhanced object detection model that integrates the ACmix attention mechanism into the YOLOv10 framework to improve feature extraction and detection precision. Through experiments conducted on the SAR-aircraft and MSAR datasets, YOLOv10n-ACmix consistently outperformed YOLOv5, YOLOv8 and YOLOv10 in precision, recall, and mean Average Precision (mAP). The inclusion of the ACmix mechanism enables the model to focus more effectively on critical features, resulting in more accurate detections. Despite a slight increase in computational complexity, the significant improvements in detection performance make YOLOv10n-ACmix a robust choice for high-precision object detection in SAR images. This work highlights the potential of attention mechanisms to enhance object detection in complex, large-scale SAR image datasets.

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Multi-UAV-Assisted Emergency Caching Networks: UAV Location and NOMA Power Optimization

Yin Y., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we propose an unmanned aerial vehicles (UAVs) assisted emergency caching framework based on non-orthogonal multiple access (NOMA), involving a primary UAV and several secondary UAVs. The primary UAV obtains rescue information from a remote base station (BS) and sends it to secondary UAVs hovering at designated locations to serve ground users. Specifically, a multi-objective optimization problem is formulated to maximize the number of files that can be successfully decoded in the primary UAV and minimize the number of user clusters for secondary UAVs. The multi-objective optimization problem is decomposed into three sub-problems: primary UAV deployment, power allocation, and secondary UAV deployment. First, a primary UAV deployment scheme is proposed to maximize the number of received files subject to the constraint of the demand for the line-of-sight (LoS) link probability between the primary UAV and the ground users. Subsequently, to achieve the same goal, a closed-form solution for the NOMA power allocation factor of the files is derived. Finally, due to the limited range of services of the secondary UAVs, we propose a secondary UAV deployment scheme to minimize the number of their flights, which can save energy and reduce user latency. Extensive numerical results demonstrate that the proposed algorithms outperform the random scheme and the worst scheme.

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Multi-Dimensional Representation for Semantic Communication: A New Horizon for Customized Visualization of Shared Knowledge

Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image generative Artificial Intelligence (AI) models, specifically stable diffusion models. Unlike conventional works, our system enables the transmission of images through a physical channel by transforming them into multi-dimensional semantic representations consisting of text descriptions, low-resolution sketches, and pose information. At the receiver's end, these semantic representations are used to reconstruct the original image using a trained stable diffusion model. The benefits of our approach include reduced transmission bandwidth requirements, flexibility in reconstruction styles, adaptability to multiple receivers' preferences, and the ability to omit unwanted image elements. We present preliminary results demonstrating the feasibility and effectiveness of our method. The similarity score between the transmitted images and reconstructed ones reach values ranging between 0.015 and 0.029 in Root Mean Square Error (RMSE) and between 0.993 and 0.998 using a Siamese network.

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mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Al M.A., Shi X., Mondher B., Ohtsuki T.

IEEE International Conference on Communications    2161 - 2166 2024年

ISSN  15503607

　概要を見る

Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.

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Massive MIMO Belief Propagation Detection Using DIP with DNN-Trained Scaling Factor

Tachibana J., Bouazizi M., Ohtsuki T.

IEEE Wireless Communications and Networking Conference Wcnc 2024年

ISSN  15253511

　概要を見る

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

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Joint Optimization of User Pairing, Power Allocation and Content Server Deployment in NOMA-Assisted Wireless Caching Networks

Yin Y., Ohtsuki T., Gui G., Yuen C., Wu H.C., Sari H.

IEEE Transactions on Vehicular Technology 72 （ 12 ） 16866 - 16870 2023年12月

ISSN  00189545

　概要を見る

In this paper, we propose a joint optimization algorithm of user pairing, power allocation, and content server (CS) deployment in non-orthogonal multiple access (NOMA) assisted wireless caching networks (WCN). The main purpose of the proposed algorithm is to maximize the hit probability of users, which is determined by the hit probability of CSs and the decoding success probability of users. Since the optimization problem is non-convex and difficult to solve directly, it is decomposed into three sub-problems, user pairing, power allocation, and CS deployment. First, users are served by CSs in a hybrid NOMA manner and paired according to the power demand coefficient. Secondly, we derive the closed-form solution of the power allocation factor in the Rayleigh fading channels. Thirdly, the simulated annealing algorithm is used to optimize the deployment of CSs. Finally, simulation results confirm that the proposed joint optimization scheme outperforms the benchmark scheme.

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DRL based low carbon economic dispatch by considering power transmission safety limitations in internet of energy

Zhu R., Guan X., Zheng J., Wang N., Jiang H., Cui C., Ohtsuki T.

Internet of Things Netherlands 24 2023年12月

　概要を見る

Economic dispatch, as a crucial method for ensuring the normal operation of power systems, is typically modeled as an optimization problem and solved using solvers. The introduction of low-carbon requirements has increased the complexity of the optimization problem, as economic dispatch now needs to consider effectively reducing the emission of CO<inf>2</inf>. The utilization of renewable energy can mitigate carbon emissions during power system operation, but its inherent uncertainty poses safety risks to transmission lines between microgrids and the main grid. Hence, this paper explores the issue of low-carbon economic dispatch to ensure the reliability of power transmission. We introduce a two-stage low-carbon economic dispatch model grounded in deep reinforcement learning. In the first stage, we incorporate the transmission power limits between microgrids and the main grid as constraints, generate day-ahead dispatch strategies, and determine the weights of factors in the reward function. In the second stage, we employ two methods: adjusting the weight of the penalty for exceeding the transmission limits and adding conservative safety limit constraints, to solve the problem of transmission power exceeding limits caused by uncertainties of renewable energy and prediction errors. Three deep reinforcement learning algorithms, all rooted in the Actor–Critic structure, are employed to implement the two-stage economic dispatch model. Experimental results affirm the efficacy of the proposed model in mitigating the risk of transmission power exceeding its limit, reducing carbon emissions, and minimizing operational costs.

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An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 11 ） 9646 - 9661 2023年06月

　概要を見る

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy Q-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional Q-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

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Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

Zhao R., Wang Y., Xue Z., Ohtsuki T., Adebisi B., Gui G.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 10 ） 8645 - 8657 2023年05月

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client's predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.

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Graph Learning Empowered Situation Awareness in Internet of Energy With Graph Digital Twin

Sui L., Guan X., Cui C., Jiang H., Pan H., Ohtsuki T.

IEEE Transactions on Industrial Informatics （IEEE Transactions on Industrial Informatics）  19 （ 5 ） 7268 - 7277 2023年05月

ISSN  15513203

　概要を見る

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.

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A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

Bouazizi M., Lorite Mora A., Ohtsuki T.

Sensors （Sensors）  23 （ 5 ）  2023年03月

ISSN  14248220

　概要を見る

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

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