Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Usmani, Sara; Saboor, Abdul; Haris, Muhammad; Khan, Muneeb A.; Park, Heemin

doi:10.3390/s21155134

Cited by 121 publications

(88 citation statements)

References 105 publications

(80 reference statements)

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“…Inertial sensors were most commonly used (compared to camera-based methods) to study falling in older adults because they have the ability to collect data outside the laboratory environment [75,76]. Different makes of inertial sensors such as Dynaport Hybrid [19,30,32], OPALS [6], Delsys Inc [28,32] and Xsens [3,45,52,59] were used across studies.…”

Section: Data Collection Modality and Kinematic Variables Analysedmentioning

confidence: 99%

“…Using different sensors with various specifications (sensors' range and sample rate differences) and different sensor placements on the body can be another reason for the variability in the reported NDA measures values [77]. An inertial sensor should be placed so that the maximum movements and signals can be captured [75,76]. Overall, the possibility to use inertial sensors to determine nonlinear characteristics of gait is a promising field.…”

Section: Data Collection Modality and Kinematic Variables Analysedmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Amirpourabasi

Lamb

Chow

et al. 2022

Sensors

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Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.

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Section: Data Collection Modality and Kinematic Variables Analysedmentioning

confidence: 99%

Section: Data Collection Modality and Kinematic Variables Analysedmentioning

confidence: 99%

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Amirpourabasi

Lamb

Chow

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…In this research field, fall detection using machine learning techniques has become very important due to the aging of the population in developed countries and the increase in the cost of hospitalizations [ 1 ]. For that reason, this kind of application has been growing due to the implementation of safety measures [ 2 ] in high-risk work environments, shopping malls, hospitals, nursing homes [ 3 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition by Sequences of Skeleton Features

Ramirez

Velastín

Aguayo

et al. 2022

Sensors

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In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

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“…Numerous technologies have been proposed to detect falls as frail elderly people walk with difficulty and are at high risk of falling [ 31 ]. These technologies can be classified into two types: (1) non-portable systems; (2) systems based on sensors and portable devices [ 32 ]. Non-portable systems use environmental sensors located in the monitoring area, typically artificial vision systems based on cameras [ 33 ] or floor sensors systems (pressure, vibration, capacitive, etc.)…”

Section: Discussionmentioning

confidence: 99%

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

Calvillo-Arbizu

Naranjo-Hernández

Barbarov-Rostán

et al. 2021

IJERPH

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Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.

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Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Cited by 121 publications

References 105 publications

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Human Activity Recognition by Sequences of Skeleton Features

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

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