Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?

Pech, Marion; Sauzéon, Hélène; Yebda, Thinhinane; Benois-Pineau, Jenny; Amieva, Hélène

doi:10.2196/29744

Cited by 16 publications

(8 citation statements)

References 61 publications

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“…While the study presents the first link between cortical activity and sensory reweighting in PD, caution is necessary due to the relatively small sample size and the VR-SOT used in this study 27 . Many VR systems currently on the market (e.g., Bertec®, Virtualis®, UprightVR) or used for research have found the VR-SOT to be reliable compared to traditional SOT 44,45 . Future studies should encompass larger sample sizes, a wider range of PD severities, and subtypes for a more comprehensive understanding how PD affects cortical processes of sensory reweighting.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson’s Disease

Sadeghi,

Bristow,

Fakorede

et al. 2024

Preprint

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Aims: Balance requires the cortical control of visual, somatosensory, and vestibular inputs. The aim of this cross-sectional study was to compare the contributions of each of these systems on postural control and cortical activity using a sensory reweighting approach between participants with Parkinsons disease (PD) and controls. Methods: Ten participants with PD (age: 72 plus or minus 9; 3 women; Hoehn & Yahr: 2 [1.5 to 2.50]) and 11 controls (age: 70 plus or minus 3; 4 women) completed a sensory organization test in virtual reality (VR-SOT) while cortical activity was being recorded using electroencephalography (EEG). Conditions 1 to 3 were completed on a stable platform; conditions 4 to 6 on a foam. Conditions 1 and 4 were done with eyes open; conditions 2 and 5 in a darkened VR environment; and conditions 3 and 6 in a moving VR environment. Linear mixed models were used to evaluate changes in center of pressure (COP) displacement and EEG alpha and theta/beta ratio power between the two groups across the postural control conditions. Condition 1 was used as reference in all analyses. Results: Participants with PD showed greater COP displacement than controls in the anteroposterior (AP) direction when relying on vestibular input (condition 5; p<0.0001). The mediolateral (ML) COP sway was greater in PD than in controls when relying on the somatosensory (condition 2; p = 0.03), visual (condition 4; p = 0.002), and vestibular (condition 5; p < 0.0001) systems. Participants with PD exhibited greater alpha power compared to controls when relying on visual input (condition 2; p = 0.003) and greater theta/beta ratio power when relying on somatosensory input (condition 4; p = 0.001). Conclusions: PD affects reweighting of postural control, exemplified by greater COP displacement and increased cortical activity. Further research is needed to establish the temporal dynamics between cortical activity and COP displacement.

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Section: Discussionmentioning

confidence: 99%

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson’s Disease

Sadeghi,

Bristow,

Fakorede

et al. 2024

Preprint

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“…Moreover, a variety of intelligent solutions of ML and DL algorithms, IoT devices, and imaging techniques have been proposed in previous studies to deal with the problem of elderly people falling [15,16]. Intelligent fall detection methods can employ vision, sound, or a wearable device to detect falls.…”

Section: Related Workmentioning

confidence: 99%

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Karar¹,

Reyad²,

Shehata³

2023

Computer Systems Science and Engineering

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This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

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“…The evolution of modern technologies developed by researchers to identify and prevent falls in elderly patients has progressed significantly over the past few years. Several sophisticated techniques have been presented to tackle the issue of senior people falling [5,12,13]. These solutions include the use of IoT devices, ML and deep learning algorithms, and imaging training methods.…”

Section: Related Workmentioning

confidence: 99%

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Reyad,

Ibrahim Shehata,

Esmail Karar

2023

Computers, Materials &Amp; Continua

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Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models-K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

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Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?

Cited by 16 publications

References 61 publications

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson’s Disease

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson’s Disease

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

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