Development of a Wearable-Sensor-Based Fall Detection System

Wu, Falin; Zhao, Hongchao; Zhao, Yan; Zhong, Haibo

doi:10.1155/2015/576364

Cited by 162 publications

(121 citation statements)

References 24 publications

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“…The required sensor for data acquisition is a smart-phone's built-in camera 12 . Such a technology can be integrated with other sensors such as fall detection systems powered by complex algorithm 13 . This will deliver efficient, affordable and more robust health care monitoring system for much older patients with chronic cardiovascular disease as suggested in 14 .…”

Section: Resultsmentioning

confidence: 99%

Real Time Telemedical Health Care Systems with Wearable Sensors

Fajingbesi

Olanrewaju

Pampori

et al. 2017

AJPRHC

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The time between detection and response to chronic diseases could go a long way in saving lives. The current trend in health monitoring systems is to move from the hospital centered device to eventually portable personal devices. Hence, Telemedical health care involves the remote delivery of medical care service to either out-of-hospital or admitted patients through wireless network and computer information technology. This paper systematically reviews the most recent works in telemedical health care system to propose a more efficient model. The focus is more on wearable sensors and devices with most attention given to cardiovascular patients in recent times. The huge literature available reflects the size of activity and attention given to telemedicine. The reviewed works are published within the last five years. Furthermore, the proposed systems are compared in terms of their connectivity, targeted application, type of sensor used, etc. Our study reveals Telemedicine to be a profound field with researchers from multidisciplinary sectors. However, there are still many gaps that need to be filled before maturity. Factors such as efficient wireless transmission, cyber data security, sensor design and integration, device miniaturization and intelligent algorithm for multi parameter data fusion require further considerations.

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Section: Resultsmentioning

confidence: 99%

Real Time Telemedical Health Care Systems with Wearable Sensors

Fajingbesi

Olanrewaju

Pampori

et al. 2017

AJPRHC

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“…In Reference [50], a novel fall detection algorithm was developed to monitor movements and to distinguish a fall from regular daily activities of patients using a wearable device. In case the patient falls, the wearable device automatically transmits the patient's location and requests caregivers for assistance.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies such as References [54][55][56] adopted multiple-input multiple-output array [50] and Doppler radar [51,52] to sense and identify elderly fall and no-fall situations. However, the radar method has the following limitations and challenges: (i) it responds to any movement from other humans or non-human sources; (ii) the radar is exposed to jitter, which generates time-dependent additional noise and false person tracking; (iii) the detected person must be within the position of the antenna beam width; (iv) the reflected signal caused by the target is blocked by furniture; and (v) the person detection distance might represent a restriction, where the reflected signal is weak when the distance is increased [56].…”

Section: Related Workmentioning

confidence: 99%

“…The previous works are plotted on the x-axis, and the estimated battery life of the sensor node is plotted on the y-axis, as shown in Figure 27. For a fair comparison between the proposed FDS and previous works [24,50,68,71,87,90,[115][116][117][118][119][120][121], the battery life of the proposed systems in the previous works was recalculated based on 1000 mAh. Most of these works are similar to our proposed system because they use different fall detection algorithms to monitor elderly fall events or activities.…”

Section: Power Consumption Comparisonmentioning

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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“…For example, the work of [44] uses a wearable triaxial accelerometer to measure the body motion and recognizes falls via one-class support vector machine. The work of [41] develops a wearable system (mainly based on the accelerometer and GPS) to detect and localize falls in the wild. Wearable sensors enable measuring physical attributes of the human body in a precise and real-time manner.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls

Zhang

Neumann

2019

Lecture Notes in Computer Science

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Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human action analysis, based on which a number of fall detection methods have been proposed. Despite their highly effective performances, the behaviors of how the convolutional nets recognize falls are still not clear. In this paper, instead of proposing a novel approach, we perform a systematical empirical study, attempting to investigate the underlying fall recognition process. We propose four tasks to investigate, which involve five types of input modalities, seven net instances and different training samples. The obtained quantitative and qualitative results reveal the patterns that the nets tend to learn, and several factors that can heavily influence the performances on fall recognition. We expect that our conclusions are favorable to proposing better deep learning solutions to fall detection systems.

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Development of a Wearable-Sensor-Based Fall Detection System

Cited by 162 publications

References 24 publications

Real Time Telemedical Health Care Systems with Wearable Sensors

Real Time Telemedical Health Care Systems with Wearable Sensors

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

An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls

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