Fall detection and fall risk assessment in older person using wearable sensors: A systematic review

Bet, Patrícia; Castro, Paula Costa; Ponti, Moacir Antonelli

doi:10.1016/j.ijmedinf.2019.08.006

Cited by 133 publications

(95 citation statements)

References 77 publications

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“…With increase of both the size of the aging population in the US and healthcare costs, sensors can be useful for ''aging in place'' programs that enable older adults to stay home and monitor their chronic conditions. They can be used to detect falls [5], cardiac arrest [3], and fatigues and depression [6], [7], among others. Sensor data can also help generate predictive algorithms that lead to an early diagnosis of diseases.…”

Section: A Current State Of Knowledgementioning

confidence: 99%

Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

2020

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This study combines wearable sensors, weather data, and self-reported mood surveys to assess mental stress on older adults from heat experience. It is designed as a pilot and feasibility study in preparation for a large-scale experiment of older adults' mental wellbeing during extreme heat events. Results show that on-body temperatures from two i-Button sensors coupled with heart rate monitored from a smart watch are important indicators to evaluate individualized heat stress given a relatively uniform outdoor temperature. Furthermore, assessing their mood in their own environment demonstrates potential for understanding mental wellbeing that can change with varying time and location.

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Section: A Current State Of Knowledgementioning

confidence: 99%

Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

2020

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“…Its maximum transmitting distance is about 100 m, and its data transmission rate is up to 250 kb/s. According to Bet et al [ 21 ], the sensors used in most fall detection studies have sampling frequencies between 40 Hz and 200 Hz. To ensure that high-frequency components are not discarded and the amount of data generated is not too large, the sampling frequency of the designed sensing module is set to 100 Hz.…”

Section: Sensing Motion Datamentioning

confidence: 99%

An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

Liu

Hou

Jian

et al. 2020

Sensors

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A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.

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“…There are several clinical tests and functional scales, including the Timed Up and Go Test (TUGT), Unipedal Standing Test, and Berg Balance Scale, that allow for assessments of balance, gait, and risk of falling [ 5 , 35 ]. The use of sensors can improve the data quality of these tests and scales [ 36 , 37 ]. Additionally, functional tests of the inner ear, such as videonystagmography or the Video Head Impulse Test, are essential to identify and measure balance disorder cases, including an age-related decline in balance function (prebyvestibulopathy) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

Gašpar¹,

Lapão²

2021

J Med Internet Res

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Background The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed.

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Fall detection and fall risk assessment in older person using wearable sensors: A systematic review

Cited by 133 publications

References 77 publications

Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather

An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

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