Instrumented timed up and go: Fall risk assessment based on inertial wearable sensors

2016 ◽  
Author(s):  
Joana Silva ◽  
Ines Sousa
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Wearable Sensors ◽  
Timed Up And Go ◽  
Fall Risk Assessment

Is Fall Risk Systematically Evaluated in Memory Clinics? A National Survey of Practice in France

2021 ◽  
pp. 1-9
Author(s):  
Victoire Leroy ◽  
Yaohua Chen ◽  
Naiara Demnitz ◽  
Florence Pasquier ◽  
Pierre Krolak-Salmon ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Standardized Tests ◽  
Fall Risk ◽  
Routine Practice ◽  
Cross Sectional Survey ◽  
Online Questionnaire ◽  
Timed Up And Go ◽  
Major Health Problem ◽  
Fall Risk Assessment ◽  
Memory Clinics

Background: Falls are a major health problem in older persons but are still under-diagnosed and challenging to prevent. Current guidelines do not target high-risk populations, especially people living with dementia. In France, people with neurocognitive disorders are mainly referred to memory clinics (MCs). Objective: We aimed to survey the routine practice of physicians working in MCs regarding fall risk assessment. Methods: We conducted a cross-sectional survey in France from January to May 2019 among physicians working in MCs, through an anonymous online questionnaire: twenty-seven questions about the physician’s background and their practice of fall risk assessment, especially use of clinical and paraclinical tools. We compared the results according to the age and the specialty of the physician. Results: We obtained 171 responses with a majority of women (60%) and geriatricians (78%). All age classes and all French regions were represented. Most of respondents (98.8%) stated that they address gait and/or falls in outpatient clinic and 95.9%in day hospitals. When asked about how they assess fall risk, fall history (83%) and gait examination (68.4%) were the most widely used, while orthostatic hypotension (24%) and clinical standardized tests (25.7%) were less common. Among standardized tests, One-leg Balance, Timed Up and Go Test, and gait speed measurements were the most used. Geriatricians had more complete fall risk assessment than neurologists (e.g., 56%versus 13%for use of standardized tests, p <  0.0001). Conclusion: Almost all physicians addressed the question of fall in MC, but practices are widely heterogeneous. Further investigations are needed to standardize fall risk assessment in MCs.

scholarly journals Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1338
Author(s):  
Wojciech Tylman ◽  
Rafał Kotas ◽  
Marek Kamiński ◽  
Paweł Marciniak ◽  
Sebastian Woźniak ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Low Cost ◽  
Body Movement ◽  
Sole Source ◽  
Timed Up And Go ◽  
True Negative ◽  
Fall Risk Assessment ◽  
Positive Ratio ◽  
Mobility Test

This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.

scholarly journals Automated fall risk assessment of elderly using wearable devices

2020 ◽  
Vol 7 ◽  
pp. 205566832094620
Author(s):  
Marian Haescher ◽  
Wencke Chodan ◽  
Florian Höpfner ◽  
Gerald Bieber ◽  
Mario Aehnelt ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Human Error ◽  
Visual Analysis ◽  
Ground Truth ◽  
Wearable Devices ◽  
Healthcare Sector ◽  
Observational Assessment ◽  
Timed Up And Go ◽  
Fall Risk Assessment

Introduction Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. Methods In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automated algorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. Results There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. Conclusions The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

scholarly journals Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time–Frequency Representations of the Timed Up and Go Test

2021 ◽  
Vol 12 ◽  
Author(s):  
Shih-Hai Chen ◽  
Chia-Hsuan Lee ◽  
Bernard C. Jiang ◽  
Tien-Lung Sun
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Learning Algorithm ◽  
The Elderly ◽  
Developed Countries ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Fall Risk Assessment ◽  
Time Frequency ◽  
Stacked Autoencoder

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls. However, continuous fall risk monitoring requires extensive healthcare and clinical resources. Our objective is to develop a method suitable for remote and long-term health monitoring of the elderly for mobility impairment and fall risk without the need for an expert. We employed time–frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according to the criteria of the timed up and go test (TUG). The time series signal of the triaxial accelerometer can be transformed by TFA to obtain richer image information. On the basis of the TUG criteria, the semi-supervised SAE model was able to achieve high predictive accuracies of 89.1, 93.4, and 94.1% for the vertical, mediolateral and anteroposterior axes, respectively. We believe that deep learning can be used to analyze triaxial acceleration data, and our work demonstrates its applicability to assessing the mobility and fall risk of the elderly.

scholarly journals Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5373
Author(s):  
Ivana Kiprijanovska ◽  
Hristijan Gjoreski ◽  
Matjaž Gams
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
Measurement Unit ◽  
Fall Risk Assessment ◽  
Abnormal Gait ◽  
Sensor Signals ◽  
Using Data ◽  
Gait Abnormalities

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.

scholarly journals Validity of the GAITRite Walkway Compared to Functional Balance Tests for Fall Risk Assessment in Geriatric Outpatients

Geriatrics ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 77
Author(s):  
Johannes Riis ◽  
Stephanie M. Byrgesen ◽  
Kristian H. Kragholm ◽  
Marianne M. Mørch ◽  
Dorte Melgaard
Keyword(s):  
Risk Assessment ◽  
Dual Task ◽  
Fall Risk ◽  
Timed Up And Go ◽  
Balance Test ◽  
Fall Risk Assessment ◽  
Functional Balance ◽  
Balance Tests ◽  
Gait Parameters ◽  
Task Conditions

This study examined the concurrent validity between gait parameters from the GAITRite walkway and functional balance test commonly used in fall risk assessment. Patients were sampled from one geriatric outpatient clinic. One physiotherapist evaluated the patients on the GAITRite walkway with three repetitions in both single- and dual-task conditions. Patients were further evaluated with Bergs Balance scale (BBS), Dynamic Gait index (DGI), Timed Up and Go (TUG), and Sit To Stand test (STS). Correlations between quantitative gait parameters and functional balance test were analyzed with Spearman’s rank correlations. Correlations strength was considered as follows: negligible <0.1, weak 0.10–0.39, moderate 0.40–0.69, and strong ≥0.70. We included 24 geriatric outpatients in the study with a mean age of 80.6 years (SD: 5.9). Patients received eight (SD: 4.5) different medications on average, and seven (29.2%) patients used walkers during ambulation. Correlations between quantitative gait parameters and functional balance test ranged from weak to moderate in both single- and dual-task conditions. Moderate correlations were observed for DGI, TUG, and BBS, while STS showed weak correlations with all GAITRite parameters. For outpatients analyzed on the GAITRite while using walkers, correlations showed no clear pattern across parameters with large variation within balance tests.

scholarly journals Validity of balance and mobility screening tests for assessing fall risk in COPD

2020 ◽  
Vol 17 ◽  
pp. 147997312092253
Author(s):  
Rachel McLay ◽  
Renata Noce Kirkwood ◽  
Ayse Kuspinar ◽  
Julie Richardson ◽  
Joshua Wald ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Functional Limitation ◽  
Forced Expiratory Volume ◽  
Screening Tools ◽  
Screening Tests ◽  
Chronic Obstructive ◽  
Timed Up And Go ◽  
Fall Risk Assessment ◽  
Increased Risk

People with chronic obstructive pulmonary disease (COPD) have balance impairments and an increased risk of falls. The psychometric properties of short balance tests to inform fall risk assessment in COPD are unknown. Our objective was to determine the validity (concurrent, convergent, and known-groups) of short balance and mobility tests for fall risk screening. Participants with COPD aged ≥ 60 years attended a single assessment. Correlation coefficients described the relationships between the Brief Balance Evaluation Systems Test (Brief BESTest), Single-Leg Stance (SLS), Timed Up and Go (TUG), and Timed Up and Go Dual-Task (TUG-DT) tests, with the comprehensive Berg Balance Scale (BBS), chair-stand test, and measures of exercise tolerance, functional limitation, disability, and prognosis. Independent t-tests or Mann–Whitney U tests were used to examine differences between groups with respect to fall risk. Receiver operating characteristic curves examined the ability of the screening tests to identify individuals with previous falls. A total of 86 patients with COPD completed the study (72.9 ± 6.8 years; forced expiratory volume in 1 second: 47.3 ± 20.3% predicted). The Brief BESTest identified individuals who reported a previous fall (area under the curve (AUC) = 0.715, p = 0.001), and the SLS showed borderline acceptable accuracy in identifying individuals with a fall history (AUC = 0.684, p = 0.004). The strongest correlations were found for the Brief BESTest and SLS with the BBS ( r = 0.80 and r = 0.72, respectively) and between the TUG and TUG-DT with the chair-stands test ( r = 0.73 and r = 0.70, respectively). The Brief BESTest and SLS show the most promise as balance screening tools for fall risk assessment in older adults with COPD. These tests should be further evaluated prospectively.

Effectiveness of the “Timed Up and Go” (TUG) and the Chair test as screening tools for geriatric fall risk assessment in the ED

2019 ◽  
Vol 37 (3) ◽  
pp. 457-460 ◽  
Author(s):  
Richard B. Chow ◽  
Andre Lee ◽  
Bryan G. Kane ◽  
Jeanne L. Jacoby ◽  
Robert D. Barraco ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Screening Tools ◽  
Timed Up And Go ◽  
Fall Risk Assessment
Sign in / Sign up

Export Citation Format

Share Document