scholarly journals Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4537 ◽  
Author(s):  
O’Brien ◽  
Hidalgo-Araya ◽  
Mummidisetty ◽  
Vallery ◽  
Ghaffari ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
Sensor Data ◽  
Gait Velocity ◽  
Clinical Test ◽  
Time Step ◽  
Timed Up And Go ◽  
High Resolution Data ◽  
Increased Risk

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20–70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

scholarly journals Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5930
Author(s):  
Tomas Mendoza ◽  
Chia-Hsuan Lee ◽  
Chien-Hua Huang ◽  
Tien-Lung Sun
Keyword(s):  
Random Forest ◽  
Postural Stability ◽  
Inertial Sensor ◽  
Short Form ◽  
Sensor Data ◽  
Permutation Entropy ◽  
Elderly Subjects ◽  
Clinical Test ◽  
Timed Up And Go ◽  
Axial Acceleration

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.

THE USE OF POSTUROGRAPHY IN INVESTIGATING THE RISK OF FALLING IN FRAIL OR PREFRAIL OLDER PEOPLE WITH DIABETES

2019 ◽  
pp. 1-7
Author(s):  
H. Domergue ◽  
L. Rodríguez-Mañas ◽  
O. Laosa Zafra ◽  
K. Hood ◽  
D. Gasq ◽  
...  
Keyword(s):  
Older People ◽  
Walking Speed ◽  
Analysis Data ◽  
Diabetic Patients ◽  
Clinical Test ◽  
Risk Of Falling ◽  
Timed Up And Go ◽  
Increased Risk ◽  
Risk Of Falls ◽  
Risk Patients

Background: In older people, diabetes is associated with an increased risk of falls and frailty. The value of using posturography for evaluating the risk of falling is unclear. In theory, a time-scale analysis should increase the metrological properties of the posturography assessment. Objectives: This study aimed to determine which posturographic parameters can be used to identify fall-risk patients in a frail diabetic older population and to assess their interest in comparison to usual clinical trials for gait and balance. Design: This is a prospective observational cohort. Settings: frail or pre-frail diabetic patients, in Bordeaux, France. Participants: 84 patients were included in the study (mean age 80.09 years, 64.5% of men).Criteria for inclusion were: age over 70 years, diabetes mellitus for over 2 years, and at least one of the Fried’s frailty criteria. Measurements: Gait and balance assessments were undertaken at baseline: Static posturography, the timed up and go test, short physical performance battery, and (gait) walking speed. Raw data from posturography were used for wavelet analysis. Data on self reported new falls were collected prospectively during 6 months. Results: The posturography parameter most useful was area of 90% confidence ellipse of statokinesigram (COP90area): area under the ROC curve AUC = 0.617 (95% CI, 0.445-0.789) and OR=1.003 (95%CI 1.000-1.005) p =0.05. The optimum clinical test was the time to walk over 4m AUC=0.735 (95%CI, 0.587-0.882) and OR=1.42 (95%CI 1.08-1.87) p= 0.013. Conclusion: Posturography has limited utility for assessment of falls risk in frail older people with diabetes. Gait and balance clinical assessments such as walking speed continue to retain their value.

A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data From the Timed-Up-and-Go Test in a Community Setting

2020 ◽  
Vol 20 (16) ◽  
pp. 9339-9350 ◽  
Author(s):  
Yu-Cheng Hsu ◽  
Yang Zhao ◽  
Kuang-Hui Huang ◽  
Ya-Ting Wu ◽  
Javier Cabrera ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Fall Risk ◽  
Inertial Sensor ◽  
Community Setting ◽  
Sensor Data ◽  
Timed Up And Go ◽  
Novel Approach

scholarly journals SENSOR-BASED ASSESSMENT OF FALLS RISK OF THE TIMED UP AND GO IN REAL-WORLD SETTINGS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S10-S10
Author(s):  
Charlene C Quinn ◽  
Barry R Greene ◽  
Killian McManus ◽  
Stephen J Redmond ◽  
Brian Caulfield
Keyword(s):  
Fall Risk ◽  
Population Based ◽  
Falls Prevention ◽  
Sensor Data ◽  
Gait Velocity ◽  
Community Settings ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Slow Walking Speed

Abstract Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings. Second objective is to generate individual targeted falls prevention programs. 14,611 QTUG records from 8,521 participants (63% female) (72.7±10.7 years) available for analysis. QTUG time was 13.9±7.4 s; gait velocity was 101.9±32.5 cm/s. 25.8% of patients reported falling in previous 12 months; 26.2% of patients were at high fall risk. 21.5% not reporting a fall, were high fall risk. Participants had slow walking speed (29.8%); high gait variability (19.8%); problems with transfers (17.5%). Easily captured and interpreted sensor data is useful in a population-based approach to quantify falls risk stratification.

scholarly journals Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0155984 ◽  
Author(s):  
Danique Vervoort ◽  
Nicolas Vuillerme ◽  
Nienke Kosse ◽  
Tibor Hortobágyi ◽  
Claudine J. C. Lamoth
Keyword(s):  
Multivariate Analyses ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Aging Effects ◽  
Timed Up And Go

scholarly journals Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors

2020 ◽  
Vol 10 (19) ◽  
pp. 6931
Author(s):  
Chia-Hsuan Lee ◽  
Chi-Han Wu ◽  
Bernard C. Jiang ◽  
Tien-Lung Sun
Keyword(s):  
Domain Knowledge ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Short Form ◽  
Berg Balance Scale ◽  
Multiscale Entropy ◽  
Clinical Test ◽  
Test Results ◽  
Timed Up And Go ◽  
Clinical Tools

The results obtained by medical experts and inertial sensors via clinical tests to determine fall risks are compared. A clinical test is used to perform the whole timed up and go (TUG) test and segment-based TUG (sTUG) tests, considering various cutoff points. In this paper, (a) t-tests are used to verify fall-risk categorization; and (b) a logistic regression with 100 stepwise iterations is used to divide features into training (80%) and testing sets (20%). The features of (a) and (b) are compared, measuring the similarity of each approach’s decisive features to those of the clinical-test results. In (a), the most significant features are the Y and Z axes, regardless of the segmentation, whereas sTUG outperforms TUG in (b). Comparing the results of (a) and (b) based on the overall TUG test, the Z axis multiscale entropy (MSE) features show significance regardless of the approach: expert opinion or logistic prediction. Among various clinical test combinations, the only commonalities between (a) and (b) are the Y-axis MSE features when walking. Thus, machine learning should be based on both expert domain knowledge and a preliminary analysis with objective screening. Finally, the clinical test results are compared with the inertial sensor results, prompting the proposal for multi-oriented data analysis to objectively verify the sensor results.

scholarly journals A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4334
Author(s):  
Guillermo García-Villamil ◽  
Marta Neira-Álvarez ◽  
Elisabet Huertas-Hoyas ◽  
Antonio Ramón-Jiménez ◽  
Cristina Rodríguez-Sánchez
Keyword(s):  
Older Adults ◽  
Concurrent Validity ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
The Elderly ◽  
Gait Pattern ◽  
Mobile App ◽  
Estimation Accuracy ◽  
Timed Up And Go ◽  
Cross Sectional

The high prevalence of falls and the enormous impact they have on the elderly population is a cause for concern. We aimed to develop a walking-monitor gait pattern (G-STRIDE) for older adults based on a 6-axis inertial measurement (IMU) with the application of pedestrian dead reckoning algorithms and tested its structural and clinical validity. A cross-sectional case–control study was conducted with 21 participants (11 fallers and 10 non-fallers). We measured gait using an IMU attached to the foot while participants walked around different grounds (indoor flooring, outdoor floor, asphalt, etc.). The G-STRIDE consisted of a portable inertial device that monitored the gait pattern and a mobile app for telematic clinical analysis. G-STRIDE made it possible to measure gait parameters under normal living conditions when walking without assessing the patient in the outpatient clinic. Moreover, we verified concurrent validity with convectional outcome measures using intraclass correlation coefficients (ICCs) and analyzed the differences between participants. G-STRIDE showed high estimation accuracy for the walking speed of the elderly and good concurrent validity compared to conventional measures (ICC = 0.69; p < 0.000). In conclusion, the developed inertial-based G-STRIDE can accurately classify older people with risk to fall with a significance as high as using traditional but more subjective clinical methods (gait speed, Timed Up and Go Test).

scholarly journals Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 54
Author(s):  
Barry R. Greene ◽  
Isabella Premoli ◽  
Killian McManus ◽  
Denise McGrath ◽  
Brian Caulfield
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Strong Association ◽  
Sensor Data ◽  
Clinical Population ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Serious Injury

People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

scholarly journals Anemia, sarcopenia, physical activity, and the risk of tuberculosis in the older population: a nationwide cohort study

2021 ◽  
Vol 12 ◽  
pp. 204062232110159
Author(s):  
Jung Eun Yoo ◽  
Dahye Kim ◽  
Hayoung Choi ◽  
Young Ae Kang ◽  
Kyungdo Han ◽  
...  
Keyword(s):  
Physical Activity ◽  
Screening Program ◽  
Chronic Obstructive ◽  
Older Population ◽  
Timed Up And Go ◽  
Obstructive Pulmonary Disease ◽  
Mild Anemia ◽  
Increased Risk ◽  
Male Sex

Background: The aim of this study was to investigate whether physical activity, sarcopenia, and anemia are associated an with increased risk of tuberculosis (TB) among the older population. Methods: We included 1,245,640 66-year-old subjects who participated in the National Screening Program for Transitional Ages for Koreans from 2009 to 2014. At baseline, we assessed common health problems in the older population, including anemia and sarcopenia. The subjects’ performance in the timed up-and-go (TUG) test was used to predict sarcopenia. The incidence of TB was determined using claims data from the National Health Insurance Service database. Results: The median follow-up duration was 6.4 years. There was a significant association between the severity of anemia and TB incidence, with an adjusted hazard ratio (aHR) of 1.28 [95% confidence interval (CI), 1.20–1.36] for mild anemia and 1.69 (95% CI, 1.51–1.88) for moderate to severe anemia. Compared with those who had normal TUG times, participants with slow TUG times (⩾15 s) had a significantly increased risk of TB (aHR 1.19, 95% CI, 1.07–1.33). On the other hand, both irregular (aHR 0.88, 95% CI 0.83–0.93) and regular (aHR 0.84, 95% CI, 0.78–0.92) physical activity reduced the risk of TB. Male sex, lower income, alcohol consumption, smoking, diabetes, and asthma/chronic obstructive pulmonary disease increased the risk of TB. Conclusion: The risk of TB among older adults increased with worsening anemia, sarcopenia, and physical inactivity. Physicians should be aware of those modifiable predictors for TB among the older population.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data
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