A Smart Device Enabled System for Autonomous Fall Detection and Alert

Jian, He; Hu, Chen; Wang, Xiaoyi

doi:10.1155/2016/2308183

Cited by 55 publications

(23 citation statements)

References 30 publications

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“…The k-NN algorithm was presented in Reference [35] to develop a smartphone device for fall detection and warning. The proposed system consists of a smartphone and a wearable motion sensor platform integrated with a gyroscope, a three-axis accelerometer, a microcontroller, and a Bluetooth module.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

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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“…Current solutions to the fall detection problem can be roughly divided into two categories: non-computer vision-based methods and computer vision-based methods: 5,6 (1) Non-computer vision-based methods. There are many non-computer vision-based methods of fall detection, such as sensitive floor tiles, 7 simple sensors, 8 and wearable sensors. [9][10][11] As falls cannot be detected at locations not equipped with specialized tiles, these tiles should be installed everywhere in the living room.…”

Section: Related Workmentioning

confidence: 99%

Fall detection via human posture representation and support vector machine

Fan

Wang

et al. 2017

International Journal of Distributed Sensor Networks

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Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision-based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multidirectional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.

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“…Recently, various proposed techniques have been used the embedded accelerometer of a smart phone to act as a fall detector for the user. In using a wearable sensors based analysis to detect fall in [6] there has been effective use of tri-axial accelerometer, gyroscope, and Bluetooth that integrated together in motion sensor board and attached to a custom vest worn by the elderly in order to capture the reluctant acceleration and angular velocity of activities of daily living in real time. The stream data is then sent to a smart phone, which runs the proposed technique based on sliding window and the kNN algorithm.…”

Section: Wearable Devicesmentioning

confidence: 99%

A Panoramic Study of Fall Detection Technologies

2016

IJCSI

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Falls are a major risk of injury for elderly aged 65 or over, blind people, people with balance disorder and leg weakness. In this regard, assistive technology which aims to identify fall events at real time can reduce the rate of impairments and mortality. This study offer a literature research reference value for bioengineers for further research. Much of the past and the current fall detection research, the vital signals features and the way features are extracted and fed to a classifier are introduced. The study concludes with an assessment of the current technologies highlighting their critical limitations along with suggestions for future research direction in this rapidly developing field of study.

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A Smart Device Enabled System for Autonomous Fall Detection and Alert

Cited by 55 publications

References 30 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

Fall detection via human posture representation and support vector machine

A Panoramic Study of Fall Detection Technologies

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