The main part of the paper appeared in DFHS'20: Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing; while the Supplementary did not appear in the proceedings
发表信息：
Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing (DFHS 2020) Nov. 2020 pp. 6-10
Monitoring of cardiovascular activity is highly desired and can enable novel applications in diagnosing potential cardiovascular diseases and maintaining an individual's well-being. Currently, such vital signs are measured using intrusive contact devices such as an electrocardiogram (ECG), chest straps, and pulse oximeters that require the patient or the health provider to manually implement. Non-contact, device-free human sensing methods can eliminate the need for specialized heart and blood pressure monitoring equipment. Non-contact methods can have additional advantages since they are scalable with any environment where video can be captured, can be used for continuous measurements, and can be used on patients with varying levels of dexterity and independence, from people with physical impairments to infants (e.g., baby camera). In this paper, we used a non-contact method that only requires face videos recorded using commercially-available webcams. These videos were exploited to predict the health attributes like pulse rate and variance in pulse rate. The proposed approach used facial recognition to detect the face in each frame of the video using facial landmarks, followed by supervised learning using deep neural networks to train the machine learning model. The videos captured subjects performing different physical activities that result in varying cardiovascular responses. The proposed method did not require training data from every individual and thus the prediction can be obtained for the new individuals for which there is no prior data; critical in approach generalization. The approach was also evaluated on a dataset of people with different ethnicity. The proposed approach had less than a 4.6\% error in predicting the pulse rate.
This paper discusses a vision based health monitoring system which would be very easy in use and deployment. Elder and sick people who are not able to talk or walk they are dependent on other human beings for their daily needs and need continuous monitoring. The developed system provides facility to the sick or elder person to describe his or her need to their caretaker in lingual description by showing particular hand gesture with the developed system. This system uses fingertip detection technique for gesture extraction and artificial neural network for gesture classification and recognition…
University of Gour Banga（古尔邦加大学）；National University of Singapore（新加坡国立大学）；Muroran Institute of Technology（室兰工业大学）
The health-care gets huge stress in a pandemic or epidemic situation. Some diseases such as COVID-19 that causes a pandemic is highly spreadable from an infected person to others. Therefore, providing health services at home for non-critical infected patients with isolation shall assist to mitigate this kind of stress. In addition, this practice is also very useful for monitoring the health-related activities of elders who live at home. The home health monitoring, a continuous monitoring of a patient or elder at home using visual sensors is one such non-intrusive sub-area of health services at…
We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse loc…
IIT Indore（印度理工学院印多尔分校）；TCS Research and Innovation（塔塔咨询服务公司研究与创新部）
A non-invasive yet inexpensive method for heart rate (HR) monitoring is of great importance in many real-world applications including healthcare, psychology understanding, affective computing and biometrics. Face videos are currently utilized for such HR monitoring, but unfortunately this can lead to errors due to the noise introduced by facial expressions, out-of-plane movements, camera parameters (like focus change) and environmental factors. We alleviate these issues by proposing a novel face video based HR monitoring method MOMBAT, that is, MOnitoring using Modeling and BAyesian Tracking. …
In this work various methods and algorithms for face and eyes detection are examined in order to decide which of them are applicable for use in a driver fatigue monitoring system. In the case of face detection the standard Viola-Jones face detector has shown best results, while the method of finding the eye centers by means of gradients has proven to be most appropriate in the case of eyes detection. The later method has also a potential for retrieving behavioral parameters needed for estimation of the level of driver fatigue. This possibility will be examined in future work.
Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring
Department of Computer（计算机系）；Department of Information（信息系）；Department of Human Genetics（人类遗传学系）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德谷分校）；Department of Electrical and Computer Engineering（电气与计算机工程系）
WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring
Delft Center for Systems and Control (DCSC), Delft University of Technology（代尔夫特理工大学系统与控制中心）；Section of Railway Engineering, Department of Engineering Structures, Delft University of Technology（工程结构系铁路工程部）
University at Buffalo, the State University of New York（纽约州立大学布法罗分校）
Computer vision leveraging deep learning has achieved significant success in the last decade. Despite the promising performance of the existing deep models in the recent literature, the extent of models' reliability remains unknown. Structural health monitoring (SHM) is a crucial task for the safety and sustainability of structures, and thus prediction mistakes can have fatal outcomes. This paper proposes Bayesian inference for deep vision SHM models where uncertainty can be quantified using the Monte Carlo dropout sampling. Three independent case studies for cracks, local damage identificatio…
Beijing University of Posts and Telecommunications（北京邮电大学）
As elderly population grows, social and health care begin to face validation challenges, in-home monitoring is becoming a focus for professionals in the field. Governments urgently need to improve the quality of healthcare services at lower costs while ensuring the comfort and independence of the elderly. This work presents an in-home monitoring approach based on off-the-shelf WiFi, which is low-costs, non-wearable and makes all-round daily healthcare information available to caregivers. The proposed approach can capture fine-grained human pose figures even through a wall and track detailed re…
Non-Contact Health Monitoring During Daily Personal Care Routines
Department of Computer Technology and Applications（计算机技术与应用系）；National Key Laboratory of Human Factors Engineering（人因工程国家重点实验室）；Department of Computer Science and Technology（计算机科学与技术系）；Paul G. Allen School of Computer Science & Engineering（Paul G. Allen 计算机科学与工程学院）
Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integr…
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
University of Bath（巴斯大学）；Loughborough University（拉夫堡大学）
