Since the emergence of the coronavirus disease (COVID-19), more than 165 million people have been infected all around the world (as of May 2021). The COVID-19 virus is highly contagious, especially in large crowded sp...
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Since the emergence of the coronavirus disease (COVID-19), more than 165 million people have been infected all around the world (as of May 2021). The COVID-19 virus is highly contagious, especially in large crowded spaces. To prevent the infection and slow the transmission of the virus, the World Health Organization recommends wearing a mask, washing hands regularly, and maintaining a distance of at least one meter between people. To support this prevention, we propose a connected object based on LoRa technology to monitor the social distancing of one meter between people in queues. The proposed system uses infrared array sensor MLX60940 to detect people through their body temperature. Based on this output, we developed an algorithm to verify social distancing. The main advantage of this node is its ability to preserve the privacy of detected people and consume less energy compared to camera-based systems. Experimental results show that the proposed node can detect up to six persons per queue with an accuracy of 93% when the distance between people is below 80 cm.
In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) arraysensors. Alongside with the main challenge which is how to further improve the p...
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In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) arraysensors. Alongside with the main challenge which is how to further improve the performance of IR arraysensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR arraysensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors (32 x 24), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including "walking", "standing", "sitting", "lying", "falling", and the transition between the activities which is referred to as "action change". This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of acti
Among various kinds of falling prevention measures, bed exit alarm mechanism has raised serious attention recently. In particular, the recent inflow of innovative ICT advancement from Internet of Things, wearable tech...
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ISBN:
(纸本)9781538662861
Among various kinds of falling prevention measures, bed exit alarm mechanism has raised serious attention recently. In particular, the recent inflow of innovative ICT advancement from Internet of Things, wearable technology, and artificial intelligence have shed on more possibility in realizing effective bed exit alarm systems. This research proposes a deep learning algorithm to construct the bed exit detection model using monitored behavior information collected from the infrared array sensor. Based on the preliminary experiment results, the bed-exit events can be recognized with 92% accuracy, 99% for precision and 97% for recall rate. This approach also has its advantages in low device costs, less data storage needed, less spacial resolution without privacy and legal concerns, and unaffected performance in various lighting conditions.
The risk of solitary death is rising because there is an increasing number of elderly living alone in Japan. Therefore, attempts are made to watch elderly remotely from his/her family. However, these systems have prob...
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ISBN:
(纸本)9781538644584
The risk of solitary death is rising because there is an increasing number of elderly living alone in Japan. Therefore, attempts are made to watch elderly remotely from his/her family. However, these systems have problems such as difficulty in confirming the status of the elderly in real time and privacy issues. In this paper, we propose a method to detect abnormal condition using infrared array sensor.
作者：
Saito, SakiNishi, HiroakiKeio Univ
Grad Sch Sci & Technol Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan Keio Univ
Fac Sci & Technol Dept Syst Design Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan
In heating, ventilation, and air conditioning control, it is crucial to maintain the comfort of residents. Thermal comfort is typically assessed using the predicted mean vote (PMV) index. PMV depends on six factors: a...
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ISBN:
(纸本)9781728148786
In heating, ventilation, and air conditioning control, it is crucial to maintain the comfort of residents. Thermal comfort is typically assessed using the predicted mean vote (PMV) index. PMV depends on six factors: air temperature, mean radiant temperature, air velocity, air humidity, metabolic rate, and clothing insulation. Although PMV can be estimated by measuring these factors directly, this process is costly because multiple sensors are required. Furthermore, measuring metabolic rate and clothing insulation is especially costly because expensive and complex sensors are required. To solve these problems, this paper proposes a practical method for estimating PMV by estimating metabolic rate and clothing insulation using a low-cost infraredarray (IrA) sensor. In this study, an IrA sensor called "Grid-EYE" is adopted. PMV parameters other than air velocity and humidity can be measured when the proposed method and an IrA sensor are implemented in an air conditioner. Human detection is done using the temperature map captured by the sensor and their PMV values are estimated individually. Heat sources around people are also detected and their influence on PMV estimation is evaluated. Practical experiments demonstrate the validity of the proposed method by providing estimated PMV values close to theoretical values and real sensations. Therefore, the proposed method can contribute to providing comfortable living spaces and improving energy consumption and amenities efficiently.
Facing direction detection plays a critical role in human computer interaction, such as face recognition and head pose estimation in biometric identification, spatial microphone/loudspeaker devices, virtual reality ga...
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ISBN:
(纸本)9783319744216;9783319744209
Facing direction detection plays a critical role in human computer interaction, such as face recognition and head pose estimation in biometric identification, spatial microphone/loudspeaker devices, virtual reality games and etc. Currently detection methods are mainly focused on extracting specific patterns of various facial features from the user's optical images, which raises concerns on privacy invasion and these detection techniques do not usually work in the dark environment. To address these concerns, this paper proposes a pervasive solution for a coarse facing direction detection using a low pixel infrared thermopile arraysensor. Support vector machine (SVM) method is selected as the classifier. Two methods for feature extraction are compared. One is based on pre-defined features and the other is based on pre-trained convolutional neural network (CNN) model. The detection accuracy resulted from using pre-defined features reaches 87% for identifying five different facing directions up to 1.2 m. However, this accuracy is largely descended when the detection range is increased to 1.8 m. Interestingly, the accuracy resulted from using pre-trained CNN features, however, demonstrates a reliable and steady performance up to 1.8 m. The accuracy keeps above 95% at both detection ranges (1.2 and 1.8 m). This proposed solution leads to many advantages, such as low resolution leading to no intention on privacy invasion, and the low-cost intriguing a potentially large market for human computer interaction in smart home appliances control and computer games.
With the aggravating trend of aging of population, the population aged over 60 years of age is growing faster than any other age groups. Under these circumstances, the number of elderly people living alone is increasi...
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ISBN:
(纸本)9781728110899
With the aggravating trend of aging of population, the population aged over 60 years of age is growing faster than any other age groups. Under these circumstances, the number of elderly people living alone is increasing. Therefore, there is increasing expectation for elderly monitoring services which can detect their general activities or emergency situations such as having a fall. Keeping the daily fundamental activities of the elderly is also important to prevent future accidents. In this paper, from the perspective of general versatility and privacy, an activity recognition method using a low-resolution infrared array sensor is proposed. The system can overcome the limitation of exist methods such as an invasion of privacy and have a wider application. A long short-term memory classifier is applied to this system in order to improve the accuracy. Experiments show that the system successfully achieves the aim of higher-precision human motion detection.
In this paper, we propose an activity detection system using a 24 x 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 x 32, 12 x 16, and 6 x 8) and...
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In this paper, we propose an activity detection system using a 24 x 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 x 32, 12 x 16, and 6 x 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 x 8 resolution), and from 90.11% to 94.54% (for images with 12 x 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.
In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualt...
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In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualties. In order to obtain a high precision and low cost fall detection system for the elderly, a fall detection system based on infrared array sensor and multi-dimensional feature fusion is proposed in this paper. First, we propose a new data acquisition method using infrared array sensor, which effectively enlarges the detection area. Then the personnel positioning is performed before fall detection, which can ensure real-time detection while reducing computational complexity. In addition, a sliding window algorithm is developed and four representative features of a fall are extracted from the collected data, which is fitful to the online detection. Among them, the four characteristics include the change in the center of mass of the falling process, the change in the speed, the change in the area of the person, and the change in variance. Finally, based on the refined features, the support vector machine (SVM) classifier is introduced to identify falls and improve the classification accuracy. The experimental results validate that the proposed fall detection system shows good fall detection accuracy and great practicability.
Sitting posture recognition is essential in preventing work-related musculoskeletal disorders (WMSDs). WMSDs are of huge concern for office workers whose working process is averagely 81.8% sedentary. Prevailing studie...
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Sitting posture recognition is essential in preventing work-related musculoskeletal disorders (WMSDs). WMSDs are of huge concern for office workers whose working process is averagely 81.8% sedentary. Prevailing studies have utilized cameras, wearables, and pressure sensors to recognize sitting postures. The cameras and wearables can achieve accurate recognition results, while personal privacy concerns and inconvenience for long-term use impede their adoption. Meanwhile, the pressure sensors are privacy-preserving and convenient. However, they cannot accurately recognize the sitting posture with different states of the trunk, head, upper extremity, and lower extremity. Considering the pros and cons of those approaches, this study proposes a novel privacy -preserving and unobtrusive sitting posture recognition system, which combines a pressure arraysensor with another privacy-preserving sensing technology, i.e., an infraredarray (IRA) sensor. Moreover, a deep learning -based sitting posture recognition algorithm is developed, which adopts a feature-level fusion strategy and does not require a complex handcrafted feature extraction process. Based on the ergonomics studies, ten daily sitting postures with the states of different body parts are selected. This system achieved an overall 90.6% ac-curacy using the leave-subject-out validation approach based on the self-collected dataset from 21 subjects. It has a great potential for privacy-preserving and unobtrusive related applications for sitting posture management.
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