Video based automatic fall detection in indoor environment

Vaidehi, V.; Ganapathy, Kirupa; Mohan, Kiran; Aldrin, A.; Nirmal, Kumar K.

doi:10.1109/icrtit.2011.5972252

Cited by 47 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A pixel is recognized as foreground when the difference between two successive frames is significant. The most popular algorithm, which was proposed by Stauffer and Grimson [45], is based on statistical background subtraction, which employs a combination of Gaussian models to observe the probability of detecting a background pixel, x , at time t , as follows:

P (x_{t}) = \sum_{i = 1}^{K} ω_{i, t} * η (x_{t}, μ_{i, t}, σ_{i, t}^{2})

where K is the number of distributions, which is normally set to [3, 5], ω i,t and μ i,t are the weighted and mean of the Gaussian distributions, respectively,

σ_{i, t}^{2}

is the covariance matrix and η is the Gaussian probability density function. The Gaussian mixture is a stable real-time outdoor tracker and works well in various environments, such as variations in lighting, repetitive motion caused by clutter and long-term scene variation.…”

Section: Methods For Sudden Event Recognitionmentioning

confidence: 99%

“…Sudden event recognition is a subset of abnormal event recognition that requires instant mitigation. Because of the rapid development in event recognition systems [5–7], our survey focuses on low-level processing aspects of sudden event recognition. Figure 2 depicts the overall structure of video-based sudden event recognition, which is presented in this survey paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sudden Event Recognition: A Survey

Suriani

Hussain

Zulkifley

2013

Sensors

View full text Add to dashboard Cite

Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

show abstract

P (x_{t}) = \sum_{i = 1}^{K} ω_{i, t} * η (x_{t}, μ_{i, t}, σ_{i, t}^{2})

where K is the number of distributions, which is normally set to [3, 5], ω i,t and μ i,t are the weighted and mean of the Gaussian distributions, respectively,

σ_{i, t}^{2}

Section: Methods For Sudden Event Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sudden Event Recognition: A Survey

Suriani

Hussain

Zulkifley

2013

Sensors

View full text Add to dashboard Cite

show abstract

“…Currently, most research of the elder care is only for the elder daily physiological parameters [11][12] or only for the elder fall event. There are about three main methods for the elder fall event: the image research method [3][4][5], the audio signal research method [6][7] and the wearable detection device method [8][9][10]. However, we believe the elder falls with the pulse change inevitably.…”

Section: Introductionmentioning

confidence: 99%

Hardware design of a body sensor network system used for elder care

Wang

et al. 2013

2013 Ninth International Conference on Natural Computation (ICNC)

View full text Add to dashboard Cite

Falls and their corresponding physiological parameters monitoring are very important for the elder health care. A body sensor network (BSN) system working in 2.4 GHz with a star topology is proposed in this paper. The body parts of the BSN used CC2520 chips for communication, deploying an acceleration sensor node on the head, a pulse sensor node on the wrist, two plantar pressure sensor nodes on the ankle and a sink node which has the acceleration sensor function on the waist respectively. It collected the human real-time information of the motion acceleration, the plantar pressure and the pulse. The sink node communicated with a remote computer through the Bluetooth module. The BSN system test result shows it can accurately detect the fall event and the pulse change.

show abstract

“…Fall detection systems based ground tremors and ground vibration information are also used to assess the fall risk of elderly individuals [15]. A fall detection system is equipped with a camera at a fixed indoor position to analyze the collected image information, assess an individual's activity status and determine whether an elderly has fallen [16]. An image detection system identifies a fall by calculating the vertical distance between the head of an individual and the ground [17].…”

Section: Introductionmentioning

confidence: 99%

Efficient Monitoring of Fall Risk in the Elderly via Functional Data Analysis

Wang¹,

Zhou²,

Kang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: A disease screening service is a preventive healthcare service. Accurate and efficient disease examination based on continuous monitoring data obtained from diseased subjects is the basis of developing this service. The traditional disease screening method for a specific disease is designed according to the physical meaning of the collected data.Methods: In this paper, a general disease detection statistical model based on monitoring data is proposed. By analyzing the distribution of data obtained from subjects who may have certain diseases, we used functional data analysis to establish a statistical model and obtain an efficient algorithm for parameter estimation. Results: The proposed model is applied to a real example of an elderly fall risk screening service based on plantar pressure data collected from elderly individuals walking over obstacles. Reasonable intervals of the model parameters used to screen the elderly for fall risk are obtained from the training samples, which are used to estimate the fall risk of the elderly with the test samples.Conclusions: The study shows that the foot plantar pressure measured in screening tests can be characterized by functional data analysis, and a linear mixed effect model can be used when time points are fixed. The restricted maximum likelihood technique is used for parameter estimation, and a nonlinear optimization algorithm is employed to iteratively determine the model parameters. This paper is to provide a method of detecting falls in the elderly based on statistical data rather than the physical meaning of collected data.

show abstract

Video based automatic fall detection in indoor environment

Cited by 47 publications

References 2 publications

Sudden Event Recognition: A Survey

Sudden Event Recognition: A Survey

Hardware design of a body sensor network system used for elder care

Efficient Monitoring of Fall Risk in the Elderly via Functional Data Analysis

Contact Info

Product

Resources

About