A fuzzy model for human fall detection in infrared video

Sokolova, Marina; Serrano-Cuerda, Juan; Castillo, José Carlos; Fernández-Caballero, Antonio

doi:10.3233/ifs-2012-0548

Cited by 38 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Body motion is usually measured using strain and pressure sensors. Possible applications for these kinds of sensors are rehabilitation, gesture identification, gait detection or expression identification [32,33]. As an example, please consider a smartwatch application named ADAM (Advanced Daily Activity Monitor) [34].…”

Section: Wearable Sensors In Healthcarementioning

confidence: 99%

Body Area Networks in Healthcare: A Brief State of the Art

et al. 2019

Self Cite

View full text Add to dashboard Cite

A body area network (BAN) comprises a set of devices that sense their surroundings, activate and communicate with each other when an event is detected in its environment. Although BAN technology was developed more than 20 years ago, in recent years, its popularity has greatly increased. The reason is the availability of smaller and more powerful devices, more efficient communication protocols and improved duration of portable batteries. BANs are applied in many fields, healthcare being one of the most important through gathering information about patients and their surroundings. A continuous stream of information may help physicians with making well-informed decisions about a patient's treatment. Based on recent literature, the authors review BAN architectures, network topologies, energy sources, sensor types, applications, as well as their main challenges. In addition, the paper focuses on the principal requirements of safety, security, and sustainability. In addition, future research and improvements are discussed.

show abstract

Section: Wearable Sensors In Healthcarementioning

confidence: 99%

Body Area Networks in Healthcare: A Brief State of the Art

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…This has opened the door to the detection of abnormal events in an automatic manner. Fall detection is by far the most commonly faced challenge and the top topic in health environments [15,16], but there exist other challenges like monitoring Parkinson's disease [17] or even recognising emotional states [18,19].…”

Section: Related Workmentioning

confidence: 99%

“…The most traditional approach to identify specific human events by analysing images is the identification of the human's posture [29][30][31]. This can be achieved by analysing the silhouette of the person [16,25], obtaining the skeleton [32][33][34], processing the complete information of the person's image, which is the approach used in this work, or even a combination of techniques [35].…”

Section: Related Workmentioning

confidence: 99%

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

et al. 2019

Self Cite

View full text Add to dashboard Cite

Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.

show abstract

“…The condition that a human area is brighter than the surrounding areas in a thermal image is typically satisfied during night and winter, but in summer, the condition is changed, and the brightness of a human image is darker than the background during summer or on a hot day. These factors can affect the accuracy of detecting human areas in thermal images [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] and make it difficult to distinguish human areas from the background in the image.…”

Section: Introductionmentioning

confidence: 99%

Human Detection Based on the Generation of a Background Image and Fuzzy System by Using a Thermal Camera

Jeon

Kim

Hong

et al. 2016

Sensors

View full text Add to dashboard Cite

Recently, human detection has been used in various applications. Although visible light cameras are usually employed for this purpose, human detection based on visible light cameras has limitations due to darkness, shadows, sunlight, etc. An approach using a thermal (far infrared light) camera has been studied as an alternative for human detection, however, the performance of human detection by thermal cameras is degraded in case of low temperature differences between humans and background. To overcome these drawbacks, we propose a new method for human detection by using thermal camera images. The main contribution of our research is that the thresholds for creating the binarized difference image between the input and background (reference) images can be adaptively determined based on fuzzy systems by using the information derived from the background image and difference values between background and input image. By using our method, human area can be correctly detected irrespective of the various conditions of input and background (reference) images. For the performance evaluation of the proposed method, experiments were performed with the 15 datasets captured under different weather and light conditions. In addition, the experiments with an open database were also performed. The experimental results confirm that the proposed method can robustly detect human shapes in various environments.

show abstract

A fuzzy model for human fall detection in infrared video

Cited by 38 publications

References 28 publications

Body Area Networks in Healthcare: A Brief State of the Art

Body Area Networks in Healthcare: A Brief State of the Art

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

Human Detection Based on the Generation of a Background Image and Fuzzy System by Using a Thermal Camera

Contact Info

Product

Resources

About