Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Rosa, Mirko Di; Hausdorff, J.M.; Stara, Vera; Rossi, Lorena; Glynn, Liam; Casey, Monica; Burkard, Stefan; Cherubini, Antonio

doi:10.1016/j.gaitpost.2017.03.037

Cited by 46 publications

(31 citation statements)

References 47 publications

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“…The changing of patient location from one place to another can provide a future opportunity to continue the current study to further understand and capture the relevant challenges of the patient's location, especially in practical applications. [68],fall detection [115],human activities [87],fall detection [116],cardiovascular and respiratory [50],fall detection [90],fall detection [117],human activities [118],fall detection [119],human activities [24],fall detection [71],patient temperature [120],human activities [121],elderly living Proposed S-BFDA Estimated battery life (h) Figure 27. Comparison between battery life of the proposed S-BFDA and previous system.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Section: Power Consumption Comparisonmentioning

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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“…Senior falls are mainly caused by weak muscles, slow reaction and movement disturbances [3,8,17]. For mimicking seniors' motor control conditions, the young subjects with eyes closed were tested at relaxed standing (simulating weak muscles and slow reaction) through a push-fall method (movement disturbances).…”

Section: Type Of Activitymentioning

confidence: 99%

“…As the population ages, falls become one of the major health problems, not only for those with some degree of balance or mobility impairment, but also among healthy active seniors [1][2][3]. Previous studies in the USA have revealed that falls were the leading cause of external injury among the elderly, accounting for 24 percent of emergency visits [4] and responsible for 70 percent of accidental deaths in persons aged 75 years and older [1].…”

Section: Introductionmentioning

confidence: 99%

“…The estimated expenditure for treatments of fall-related injuries amounts to 20.2 billion US dollars annually [5]. Further, previous studies also revealed that one-third of people 65 years and older experience at least one fall each year [3,6,7]. This severe problem has a significant impact on personal health and medical care costs in older adults.…”

Section: Introductionmentioning

confidence: 99%

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A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

et al. 2017

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Abstract:As the population ages, falls are becoming a major health problem, not only for those with some degree of balance or mobility impairment, but also among healthy active seniors. Previous studies suggest that the degradation of human sensorimotor function related to age contributes to falls. Hip bones are among the most frequently fractured body parts resulting from falls. Hip fractures are a frequent cause of early death, functional dependence, and high medical care costs. The current prevention method is to use hip protectors. Unfortunately, it often fails to do so because the pocket containing the pad can move away from the area during falls. Additionally, some seniors refuse to use hip protectors because they find them constraining. Hence, a new protector that is only activated during a fall is much desired. The current study explored the possibility via biomechanical analyses for building a wearable sensor system that triggers a mini-airbag system during a fall, i.e., the air-pad is only present for protection when a fall occurs. The results have revealed that two sensors placed on the left and right shoulder would be best for a detection of any-direction fall and could be applied for building a wearable sensor system for prevention of hip fractures resulting from falls.

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Gangstörungen und Stürze

Bötzel

2018

Neurogeriatrie

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Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Cited by 46 publications

References 47 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

A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors

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