Fall detection in the elderly by head tracking

Yu, Miao; Naqvi, Syed Mohsen; Chambers, Jonathon A.

doi:10.1109/ssp.2009.5278566

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…Several methods use particle filters for object tracking and localization. In [7,8] the authors describe the application of particle filters for tracking moving objects using background subtraction to track human silhouettes based on color images. In [9], Rougier et al have used the head's velocity to detect the fall in visual videos by setting thresholds manually.…”

Section: Related Workmentioning

confidence: 99%

“…where S is the silhouette ellipse center, B is the upper point of the major axis and BC is the head major axis knowing that the ratio between major axis and minor axis is set to 1.2 [7]. The sole use of the seg-mentation process is not a robust method to track the head position.…”

Section: Segmentation Stepmentioning

confidence: 99%

“…At each frame t, the state vector x t of PF is defined by the center H, the size L and the orientation θ of the head extracted by the segmentation step in depth image, Eq. (7).…”

Section: Tracking Processmentioning

confidence: 99%

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Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Halima

Laferté

Cormier³

et al. 2020

ICA

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The security of elderly people living alone is a major issue. A system that detects anomalies can be useful for both individual and retirement homes. In this paper, we present an adaptive human tracking method built on particle filter, using depth and thermal information based on the velocity and the position of the head. The main contribution of this paper is the fusion of information to improve tracking. For each frame, there is a new combination of coefficients for each particle based on an adaptive weighting. Results show that the tracking method can deal with the cases of fast motion (fall), partial occultation and scale variation. To assess the impact of fusion on the tracking process, the robustness and accuracy of the method are tested on a variety of challenging scenarios with or without depth-thermal fusion.

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Section: Related Workmentioning

confidence: 99%

Section: Segmentation Stepmentioning

confidence: 99%

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Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Halima

Laferté

Cormier³

et al. 2020

ICA

View full text Add to dashboard Cite

show abstract

“…Moreover, data collected can be used to analyze impact patterns in case of fall or other emergency situations. Similarly, Miao et al (2009) proposed a fall detection approach based on head tracking using two video cameras. They constructed three-dimensional head position models with unimportant objects and background filtered out.…”

Section: Sensor Technology and Networkmentioning

confidence: 99%

Smart home technology for telemedicine and emergency management

2012

J Ambient Intell Human Comput

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With the ageing population, mobility is an important issue and it deters the elderlies to visit health clinics on a regular basis. Individuals with disabilities also face the same obstacles for their out-of-home medical visits. In addition, people living in remote areas often do not get the needed health care attention unless they are willing to spend the time, effort and cost to travel. Advances in information and telecommunication technologies have made telemedicine possible. Using the latest sensor technologies, a person's vital data can be collected in a smart home environment. The bio-information can then be transferred wirelessly or via the Internet to medical databases and the healthcare professionals. Using the appropriate sensing apparatus at a smart home setting, patients, elderlies and people with disabilities can have their health signals and information examined on a realtime and archival basis. Recovery process can be charted on a regular basis. Remote emergency alerts can be intercepted and responded quickly. Health deterioration can be monitored closely enabling corrective actions. Medical practitioners can therefore provide the necessary healthrelated services to more people. This paper surveys and compiles the state-of-the-art smart home technologies and telemedicine systems.

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“…Bu çalışma oklüzyon karşısında yeterli değildir ve ışık değişimlerinden etkilenir. [6,8]deki çalışmalar da hareket geçmişi haritası ve baş takibi ile düşme durumu algılanmaktadır. Oklüzyon karşısında yetersiz kalır.…”

Section: Introductionunclassified

Developing an occlusion-resistant automatic fall detection system for smart environments

Ozcan

Ergun

2012

2012 20th Signal Processing and Communications Applications Conference (SIU)

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ÖZETÇEBu çalışmada görüntü sensörleri ile insanların bir oda içindeki hareketlerini izleyen ve bu hareketleri düşme, ayakta durma ve diğer hareketler olarak üç gruba ayıran otomatik davranış analiz sistemi önerilmektedir. İnsan hareketlerini görüntü işleme yöntemleri ile analiz ederkenki en büyük problemler, ortamdaki ışık değişimleri, eşyalarla insan arasındaki etkileşim ve de insan görüntüsünün sürekli olarak kameradan görünmemesinden kaynaklanan veri kaybıdır. Bu problemleri aşmak için bu çalışmada iki önemli katkı yapılmaktadır. İlk olarak, RGB verileri ile beraber, derinlik sensörü de kullanılarak, objelerin üç boyutlu pozisyon ve geomertik şekilleri algılanmaktadır. İkinci olarak, şimdiye kadar ki çalışmalardan farklı olarak, ortamın kuş bakışı görüntüsü tepeye yerleştirilen bir sensör ile algılanmaktadır. Görüntüdeki insan silüetleri RGB ve derinlik verisi olarak kaydedilip, Derinlik Histogramları oluşturulur. Histogramlar ve silüetten elde edilen parametreler incelenerek, insanın o anki durumu ayakta, düşme ve diğer hareket olarak üç sınıfa ayrılır. Deneysel sonuçlar önerilen yöntemin başarılı bir şekilde uygulanmasını göstermektedir. ABSTRACT This paper proposes an occlusion resistant automatic fall detection framework for smart environments. There are two major contributions of the proposed method. First, synchronized RGB and depth data are utilized together to capture both apperance and geometrical characteristics of human silhouettes in the environment. Second, unlike existing methods, a single Kinect sensor is mounted on a ceiling and plan-view of the room is captured to avoid occlusions rising from furnitures. For each frame, silhouette of person is extracted from depth data. From silhouette data, depth histogram, bounding box, distribution of average and highest depth values are calculated. The system learns these parameters for different regions of the room to classify human poses into three categories as standing, fall down and other poses. Experimental results show successful application of the proposed framework to detect falls under complex situations.

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Fall detection in the elderly by head tracking

Cited by 14 publications

References 6 publications

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Smart home technology for telemedicine and emergency management

Developing an occlusion-resistant automatic fall detection system for smart environments

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