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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ogawa, Yuya Naito, Katsuhiro Aichi Inst Technol Grad Sch Business Adm & Comp Sci Nagoya Aichi 4640807 Japan Aichi Inst Technol Dept Informat Sci Toyota Aichi 4700392 Japan

ISBN: (纸本)9781728151861

In recent years, the rate of older people has been increasing with the aging of population. In such a situation, many older people are injured by falls every year. As a countermeasure, several studies have been proposed to inform others of a fall as a quick post-fall treatment. Therefore, fall detection methods using various sensors have been proposed. Assuming fall detection at places such as home or hospital room, a fall detection method without invasion of privacy and wearing anything is required. In this paper, we propose a fall detection method using ir array sensors. The method allows for fall detection that is inexpensive and capable of privacy protection in a non-wearable form. Also, we analyze temperature distributions using machine learning to enable quicker and more accurate fall detection. We evaluate multiple algorithms of machine learning to select best algorithm. Then, classifiers are created based on these algorithms. We calculate and compare the accuracy of these classifiers. One of the learning data is a series of temperature distribution data for 2 seconds. One temperature distribution is acquired every 0.1 seconds by ir array sensors installed on a ceiling. We prepare 1600 learning data (400 series of learning data each with 4 actions: fall, walking, lying, and none). Based on these data, classifiers are performed using multiple algorithms to determine accuracy. The most accurate algorithm is Voting classifier with 97.75% accuracy. Therefore, the evaluation result showed that the proposed method is possible to classify with high accuracy using ir array sensors based on these prepared learning data.

关键词： fall fall detection ir array sensor temperature distribution machine learning

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The Use of Thermal ir array sensor for Indoor Fall Detection

The Use of Thermal IR Array Sensor for Indoor Fall Detection

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Hayashida, Akira Moshnyaga, Vasily Hashimoto, Koji Fukuoka Univ Grad Sch Engn Jonan Ku 8-19-1 Nanakuma Fukuoka 8140180 Japan Fukuoka Univ Dept Elect Engn & Comp Sci Jonan Ku 8-19-1 Nanakuma Fukuoka 8140180 Japan

ISBN: (纸本)9781538616451

This paper presents new approach for unobtrusive indoor fall detection by an ir thermal array sensor. Unlike existing methods that run fall detection at server and require high communication and processing rates, we perform fall detection within the sensor node by a computationally inexpensive algorithm that signals the server only when a fall occurs. Experiments with prototype design show that such formulation provides robust and real-time fall detection even in a noisy environment.

关键词： assistive technology fall detection ir array sensor passive system

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A Comparison Study of Person Identification Using ir array sensors and LiDAR

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sensorS 2025年第1期25卷 271-271页

作者： Liu, Kai Bouazizi, Mondher Xing, Zelin Ohtsuki, Tomoaki Keio Univ Grad Sch Sci & Technol Yokohama 2238522 Japan Keio Univ Fac Sci & Technol Yokohama 2238522 Japan

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

关键词： person identification deep learning ir array sensor LiDAR

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Low-Resolution Infrared array sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

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INFORMATION 2022年第3期13卷 132页

作者： Bouazizi, Mondher Ye, Chen Ohtsuki, Tomoaki Keio Univ Fac Sci & Technol Yokohama Kanagawa 2238522 Japan Chiba Univ Ctr Frontier Med Engn Chiba 2638522 Japan

In this paper, we propose a method that uses low-resolution infrared (ir) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 x 24 pixels ir array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end ir array sensors with even lower resolution (16 x 12 and 8 x 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 x 12. For frames of size 8 x 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

关键词： counting deep learning healthcare indoor localization ir array sensor machine learning

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