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Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution infrared array sensor

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sensorS 2020年第20期20卷 5957.-5957.页

作者： Tateno, Shigeyuki Meng, Fanxing Qian, Renzhong Hachiya, Yuriko Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Univ Occupat & Environm Hlth Sch Hlth Sci Kitakyushu Fukuoka 8078555 Japan

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user's body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

关键词： human motion detection falling infrared array sensor privacy protection three-dimensional convolutional neural network

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Hand Gesture Recognition System for In-car Device Control Based on infrared array sensor 58

Hand Gesture Recognition System for In-car Device Control Ba...

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58th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Tateno, Shigeyuki Zhu, Yiwei Meng, Fanxing Waseda Univ Grad Sch IPS Kitakyushu Fukuoka Japan

ISBN: (纸本)9784907764678

Nowadays, with the development of automotive industry, more and more functions and devices are assembled in cars to improve driving experience. Meanwhile, traffic accidents are increasing in recent years. Operations of in-car device human machine interface (HMI) will cause lack of concentration, which is a major cause of the accidents. Common in-car device HMI systems are based on optics, acoustics, and so on, which are faced with environment limitations such as influenced by illumination conditions. In order to deal with these limitations, in this paper, an infrared array sensor is applied to construct a hand gesture recognition system for in-car devices control. The proposed system can overcome disadvantages of other systems and has a wider application. In the system, seven different shapes of hand and movement toward four directions are combined to achieve the aim of device operations. In data processing, convolutional neural network (CNN) is applied to realize recognition. Simulated experiments are conducted to verify the feasibility of this system.

关键词： infrared array sensor Hand Gesture Recognition

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Multi-Person Localization Based on a Thermopile array sensor with Machine Learning and a Generative Data Model

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sensorS 2025年第2期25卷 419-419页

作者： Klir, Stefan Lerch, Julian Benkner, Simon Khanh, Tran Quoc Tech Univ Darmstadt Lab Adapt Lighting Syst & Visual Proc Hochschulstr 4a D-64289 Darmstadt Germany

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 x 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision.

关键词： people localization thermopile array multi-person detection infrared array sensor generative IR data

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Occupancy Estimation in Buildings Based on infrared array sensors Detection

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IEEE sensorS JOURNAL 2020年第2期20卷 1043-1053页

作者： Yuan, Yazhou Li, Xin Liu, Zhixin Guan, Xinping Yanshan Univ Sch Engn Qinhuangdao 066004 Hebei Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Along with methods such as Hidden Markov Model and Linear Discriminant Analysis, environmental sensors including temperature, humidity or carbon dioxide are used to estimate indoor occupancy, which have various applications. Most of the previous studies have neglected the time-dependent character of the indoor occupancy information or real-time response of the system. In this paper, we propose to use low invasive, fast-sampling infrared array sensors to collect data from the actual scene and establish the Inhomogeneous Hidden Markov Model to capture the time dependence of occupancy for buildings occupancy estimation. First, to avoid raw sensor datas susceptibility to external influences, the non-negative matrix factorization is adopted to reduce the dimension of the raw matrix and eliminate interferences caused by environmental changes. Second, the Softmax Regression Model is used to calculate the emission probability matrix for clarifying the dynamic relationship between environmental parameters and indoor occupancy, which is weak. Third, the Forward algorithm and the Viterbi algorithm are applied to achieve online and off-line estimation. Experiments are made with real recorded data. Our performance results demonstrate that method we proposed is effective for indoor occupancy estimation.

关键词： infrared array sensor NMF IHMM softmax regression occupancy estimation

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ISFDNet: Using Low-Resolution infrared array sensors to Detect Elderly Fall in Home Elevator Scenarios

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IEEE sensorS JOURNAL 2024年第16期24卷 26994-27006页

作者： Yu, Dingxin Jiang, Xianliang Jin, Guang Lei, Fei Yang, Ze Gong, Guanghui Cui, Zehui Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Eastern Inst Technol Ningbo 315200 Peoples R China Ningbo Lilong Machinery & Elect Co Ltd Ningbo 315800 Peoples R China

Falls are a significant cause of injury in older people, and in the home elevator environment, the consequences of a fall can be more severe due to the confined and narrow space. This article focuses on the home elevator scenario. First, we construct a fall detection dataset based on infrared array sensors, considering nonhuman factors such as pets in a home environment. Second, we propose a background filtering method based on Otsu's method to eliminate the interference of nonhuman factors in the background. In addition, we introduce a new and more generalized single-frame fall detection model, ISFDNet, which shows higher fall detection precision than traditional models and achieves a precision rate of 96.64%. Finally, we adopt a single-frame detection approach combined with a sliding window-based fall detection event triggering mechanism, which ensures fall detection accuracy, reduces memory requirements, and improves inference speed;the method obtained a 100% precision rate of fall event detection.

关键词： sensor arrays sensors Fall detection Accuracy Feature extraction Elevators sensor phenomena and characterization Background filter deep learning fall detection home elevators environment infrared array sensor

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Air-Conditioning Control with Spatial Recognition Using Stereo infrared array sensors 47

Air-Conditioning Control with Spatial Recognition Using Ster...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Takayama, Yuki Saito, Saki Sakuma, Yuiko Nishi, Hiroaki Keio Univ Grad Sch Sci & Technol Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan Keio Univ Dept Syst Design Fac Sci & Technol Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781665435543

Depending on the location of the air conditioner and the shape of a room, air-conditioning control may be inefficient resulting in temperature imbalance. When attempting to solve this problem, it is vital to understand the spatial structure of a room (including its size and shape) and the location of air conditioners and then automatically control the airflow and direction according to the structure. However, such a method for recognizing spatial structures has not yet been established. In this paper, we propose a spatial recognition method using stereo infrared array sensors (SIRA sensors) installed in an air conditioner. Our system detects objects in the obtained thermal images and estimates their distances using triangulation. In addition, the room's size and shape are estimated based on the assumption that the room size lies within the detection range. The distances to the front and left/right walls were estimated in one-meter-wide classes. The estimation accuracy was compared using two types of IRA sensors: thermopile array sensors and thermal diode infrared sensors. Regarding the distance estimation of persons from the captured stereo thermal images, the average error rate was 12.5% for both types. The distance to each wall was estimated within a 1m error range for the thermal diode infrared sensor. Moreover, applications of the proposed spatial recognition to air-conditioning control were demonstrated. Specifically, we propose a method to control the airflow direction and volume by considering the room's geometry. An L-shaped room was modeled and simulated. From the results, the spatial recognition reduced the unevenness in temperature by adjusting the airflow based on the room shape. These results indicate that the proposed method can be practically used for spatial recognition to efficiently improve user comfort by controlling air-conditioning based on the spatial structure and eliminating uneven temperature.

关键词： infrared array sensor object detection distance estimation air-conditioning control

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Occupancy Estimation Using Thermal Imaging sensors and Machine Learning Algorithms

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IEEE sensorS JOURNAL 2021年第6期21卷 8627-8638页

作者： Chidurala, Veena Li, Xinrong Univ North Texas Dept Elect Engn Denton TX 75025 USA

Occupancy estimation has a broad range of applications in security, surveillance, traffic and resource management in smart building environments. Low-resolution thermal imaging sensors can be used for real-time non-intrusive occupancy estimation. Such sensors have a resolution that is too low to identify occupants, but it may provide sufficient data for real-time occupancy estimation. In this paper, we present a systematic study of three thermal imaging sensors with different resolutions, with a focus on sensor characterization, estimation algorithms, and comparative analysis of occupancy estimation performance. A unified processing algorithms pipeline for occupancy estimation is presented and the performance of three sensors are compared side-by-side. A number of specific algorithms are proposed for pre-processing of sensor data, feature extraction, and fine-tuning of the occupancy estimation algorithms. Our results show that it is possible to achieve about 99;accuracy for occupancy estimation with our proposed approach, which might be sufficient for many practical smart building applications.

关键词： sensors Thermal sensors Image sensors Estimation Imaging sensor systems sensor phenomena and characterization Classification infrared array sensor occupancy estimation thermal imaging

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Unobtrusive sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms

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IEEE sensorS JOURNAL 2018年第15期18卷 6360-6368页

作者： Chen, Zhangjie Wang, Ya Liu, Hanwei Texas A&M Univ Dept Mech Engn College Stn TX 77840 USA SUNY Stony Brook Dept Elect Engn Stony Brook NY 11790 USA

Facing direction detection plays a critical role in human-computer interaction and has a wide application in surveillance systems, driving awareness recognition, smart home appliances, computer games, and so on. Current detection methods are mainly focused on extracting specific patterns from user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in a dark environment. To address these concerns, this paper proposes an activity recognition system guided by an unobtrusive sensor (ARGUS). By using a low pixel infrared thermopile array sensor, ARGUS is capable of identifying five facing directions (left 45 degrees/90 degrees, right 45 degrees /90 degrees, and front) through the support vector machine classifier. Also two feature extraction methods are compared. One is manually-defined and the other is based on a pre-trained convolutional neural network (CNN) model. The facing direction detection accuracy resulting from manually-defined features reaches 85.3%, 90.6%, and 85.2% at detection distance of 0.6, 1.2, and 1.8 m, respectively. The level of accuracy resulted from using pre-trained CNN features demonstrates a much more reliable performance (89.1%, 95.3%, and 95.1% at distance of 0.6, 1.2, and 1.8 m, respectively). In addition, ARGUS has been successfully applied for occupancy tracking with a root mean square error of 0.19 m.

关键词： infrared array sensor facing direction detection occupancy tracking SVM CNN

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Privacy-preserving activity recognition using multimodal sensors in smart office

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 27-38页

作者： Zhang, Xiangying Zheng, Pai Peng, Tao Li, Dai Zhang, Xujun Tang, Renzhong Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Sunon Technol Co Ltd Hangzhou Peoples R China Zhejiang Univ ZJU Sunon Joint Res Ctr Smart Furniture Hangzhou Peoples R China

Developing a human activity recognition (HAR) system for employees is essential to incorporate intelligence into smart office environments, enabling various human-centered applications to enhance employees' well-being. Although remarkable progress has been made for the HAR in the smart office, several issues still exist, including lacking a privacy-preserving and unobtrusive method and demanding enhanced generalization performance across users. Therefore, a novel privacy-preserving HAR method based on multimodal sensors is investigated in this study, containing an infrared array sensor, a sensing chair, and a triaxial accelerometer built in a smartphone. The effectiveness of different multimodal combinations is examined. Moreover, a deep learning model is developed for multimodal data fusion to enhance the generalization performances across users. The model contains a residual 3D convolutional neural network (CNN) and 1D CNN for learning spatial-temporal feature representation of different modalities. Additionally, external memory units and an adaptive decision fusion operation are utilized for multimodal data fusion. Finally, extensive experiments are conducted to examine the performance of the proposed model using a self-collected dataset and the leave-one-subject-out cross-validation approach. The results verify the effectiveness of the proposed model. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Activity recognition Privacy -preserving method Multimodal data infrared array sensor Sensing chair Smartphone

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Online Estimation Method of Badminton Players' Grip Position Error Based on Particle Filtering 6

Online Estimation Method of Badminton Players' Grip Position...

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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Wang, Yulin Wu, Lihua Southwest Jiaotong Univ Dept Fdn Hope Coll Chengdu 610400 Peoples R China Chengdu Coll Arts & Sci Sch Econ & Management Chengdu 610400 Peoples R China

ISBN: (纸本)9781665432634

In order to solve the problems of low accuracy and long time consumption of traditional badminton players' grip position error estimation methods, a new online estimation method of badminton players' grip position error based on particle filter was proposed in this paper. infrared array sensor is used to collect badminton players' grip posture images, and the collected images are de-noised and segmenting. The probability density of pixel points of the processed images is estimated, and the grip posture area and the contour features of the grip posture are extracted, so as to realize the recognition of badminton players' grip posture. On this basis, the similarity of the grip pose sequence and the standard grip pose sequence were taken as two kinds of particles. By comparing the similarity of the two kinds of particles, the final online estimation result of the grip pose error was obtained. The experimental results show that the maximum error rate of badminton players' grip position recognition is only 3.96%, the error estimation accuracy is between 95.76%-98.25%, and the estimated time is between 0-0.5s.

关键词： Particle filter Badminton player Grip position On-line error estimation infrared array sensor

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