3D depth image analysis for indoor fall detection of elderly people

Yang, Lei; Ren, Yumei; Zhang, Wenqiang

doi:10.1016/j.dcan.2015.12.001

Cited by 96 publications

(51 citation statements)

References 23 publications

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“…I Avg = I Avg TL + I Avg ACC + I Avg SH + I Avg MR + I Avg ZB (8) where I Avg_TL , I Avg_ACC , I Avg_SH , I Avg_MR , and I Avg_ZB are the average current consumption of the tilt sensor, accelerometer sensor, shock sensor, microcontroller, and Zigbee model, respectively. The average current consumption of each element of the FDS can be expressed as follows:…”

Section: Power Consumption Analysismentioning

confidence: 99%

“…Several fall detection and localization systems have been developed recently. Fall detection can be categorized into three systems, namely, (i) vision-based, (ii) ambient sensor-based, and (iii) wearable sensor-based [8]. Vision-based systems employ a computer to capture images and videos of fall events.…”

mentioning

confidence: 99%

“…The sensors of the last method are attached to the subject of interest. The wearable sensor-based method usually depends on accelerometer sensors, which are connected to the body and provide an extraordinary level of obtrusiveness [8]. Accelerometers are popular wearable sensors embedded in FDSs to detect the position and movement of the subject [12].…”

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confidence: 99%

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Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Section: Power Consumption Analysismentioning

confidence: 99%

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confidence: 99%

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Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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“…Izadi et al presented KinectFusion, a system that allows users to hold a Kinect to generate 3D models of a scene in real-time [12]. Researchers argue that 3D maps have many applications, ranging from elderly support [22] robotics, telepresence, gaming and AR. They further state that they believe that the price of RGB-D cameras will decrease in the future, allowing the cameras to enhance different kinds of application scenarios, as well as human perception.…”

Section: Extending Visual Perceptionmentioning

confidence: 99%

Exploration of Alternative Vision Modes Using Depth and Thermal Cameras

Abdelrahman

Woźniak

Knierim

et al. 2018

Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia

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“…Falls are detected when the depth value of the head is close to the floor level. Other methods based on the shape of subjects have been proposed, exploiting for example a set of moment functions which approximate the human shape to an ellipse, whose coefficients are calculated to determine the direction and position of the individual [79]. In order to have a more accurate system, the centroid of human body and the angle between this and the floor plane have been calculated.…”

Section: A Fall Detection Using Depth Datamentioning

confidence: 99%

Radar and RGB-Depth Sensors for Fall Detection: A Review

et al. 2017

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Abstract-This paper reviews recent works in the literature on the use of systems based on Radar and RGB-Depth sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

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3D depth image analysis for indoor fall detection of elderly people

Cited by 96 publications

References 23 publications

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Exploration of Alternative Vision Modes Using Depth and Thermal Cameras

Radar and RGB-Depth Sensors for Fall Detection: A Review

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