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 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.

scholarly journals The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2183 ◽  
Author(s):  
Ruopeng Sun ◽  
Roberto G. Aldunate ◽  
Jacob J. Sosnoff
Keyword(s):  
Mixed Reality ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Preliminary Investigation ◽  
Functional Mobility ◽  
Timed Up And Go ◽  
Perfect Agreement ◽  
Mobility Performance ◽  
Mobility Assessment ◽  
Clinical Tools

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in older adults. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older and 12 young adults) participated in this study. An automated functional mobility assessment app was developed, based on the HoloLens platform. The mobility performance was recorded with the headset built-in sensor and reference inertial sensor (Opal, APDM) taped on the headset and lower back. The results indicate that the vertical kinematic measurements by HoloLens were in good agreement with the reference sensor (Normalized RMSE ~ 10%, except for cases where the inertial sensor drift correction was not viable). Additionally, the HoloLens-based test completion time was in perfect agreement with the clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users (without severe mobility deficit) to complete common mobility tests, and this approach has the potential to provide an objective and efficient sensor-based mobility assessment that does not require any direct research/clinical oversight.

scholarly journals Multidisciplinary protocol for the management of fibromyalgia associated with imbalance. Our experience and literature review

2019 ◽  
Vol 65 (10) ◽  
pp. 1265-1274
Author(s):  
Rita Chiaramonte ◽  
Marco Bonfiglio ◽  
Sergio Chisari
Keyword(s):  
Exercise Training ◽  
Rating Scale ◽  
Short Form ◽  
Fibromyalgia Impact Questionnaire ◽  
Training Group ◽  
Complementary Therapy ◽  
Berg Balance Scale ◽  
Clinical Findings ◽  
Timed Up And Go ◽  
Proprioceptive Training

SUMMARY OBJECTIVE We reported our multidisciplinary protocol for the management of fibromyalgia associated with imbalance. Our aim was to verify the effectiveness of a proprioceptive training program as a complementary therapy for a traditional protocol of education, mindfulness, and exercise training for the management of fibromyalgia associated with imbalance. METHODS Retrospective cohort study on 84 women, with primary fibromyalgia associated to imbalance. A group of patients performed traditional exercise training; in a second group the training was supplemented with proprioception exercises. Each session lasted from 40 to 60 minutes and was performed three times a week for 12 weeks. RESULTS After three months of training and eight months after the end of the training, the balance evaluation revealed significant differences in the comparison of the Timed Up and Go test, Berg Balance Scale, and Tinetti scale with the baseline, there was a better improvement in the proprioceptive training group (p<0.05). A reduction in pain and improvement in functional and muscular performance and quality of life were observed in both groups (p<0.05), but with no significant differences between them in the Numeric Pain Rating Scale, Fibromyalgia Impact Questionnaire, and Short Form Health Survey (p>0.05). Fifteen months after the end of the program, the effects of training were not maintained. CONCLUSION The present study revealed that training supplemented with proprioception exercises has beneficial effects on clinical findings and improves balance in patients with fibromyalgia, even if the positive results did not persist after the interruption of the rehabilitative program in the long term.

THUR 229 Inertial sensors improve traditional gait monitoring in HSP patients

2018 ◽  
Vol 89 (10) ◽  
pp. A33.2-A33
Author(s):  
McNamara Mary ◽  
Segamogaite Ruta ◽  
Shaw Pamela ◽  
McDermott Christopher ◽  
Mazzá Claudia ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Brief Pain Inventory ◽  
Timed Up And Go ◽  
Mild Disease ◽  
Gait Characteristics ◽  
Gait Monitoring ◽  
Ashworth Score

BackgroundHSP is characterised by spasticity and progressive gait impairment. There’s no reliable way to monitor gait deterioration during clinics. Optoelectronic systems have demonstrated differing characteristics between gait of HSP patients and controls. They’re expensive and impractical for use in clinic settings. Inertial sensors haven’t been used to characterise HSP gaitObjectivesStudy use of inertial sensors to identify gait characteristics that differentiate mild HSP patients from controls. To identify a gait based biomarker which can be used to monitor disease progression in a longitudinal study.MethodsNeurological examination, SPRS, Modified Ashworth score, brief pain inventory were undertaken. Instrumented timed up and go (iTUG) and instrumented 10 metre walk tests (i10) wearing an inertial sensor during clinic appointments at 6 month intervals.ResultsGait variables differentiating between patients and controls, including those with mild disease, were identified. Parameters differentiating between patients with SPG4 and SPG7 mutations were found. 8 patients were re-assessed after 6 months. Analysis did not show gait deterioration.ConclusionInertial sensors can detect differences between HSP patients and controls, including those mildly affected. They can also differentiate between patients with different mutations. Further follow up data is needed to assess whether inertial sensors can predict future gait deterioration.

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 The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment

2019 ◽  
Author(s):  
Ruopeng Sun ◽  
Roberto G. Aldunate ◽  
Jacob J Sosnoff
Keyword(s):  
Mixed Reality ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Preliminary Investigation ◽  
Functional Mobility ◽  
Timed Up And Go ◽  
Perfect Agreement ◽  
Mobility Performance ◽  
Mobility Assessment ◽  
Kinematic Measures

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in the aging population. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older, 12 young) participated in this study. An automated functional mobility assessment app was developed based on the HoloLens platform. Mobility performance was recorded with the headset built-in sensor and validated with reference inertial sensor (Opal, APDM) taped on the headset and lower back. Results indicate vertical kinematic measures by HoloLens was in good agreement with the reference sensor (Normalized RMSE ~ 10%). Additionally, the HoloLens-based test completion time was in perfect agreement with clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users to complete common mobility tests with good measurement accuracy, thus it has great potential to provide objective and efficient sensor-based mobility assessment.

scholarly journals Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1076 ◽  
Author(s):  
Wu ◽  
Lee ◽  
Jiang ◽  
Sun
Keyword(s):  
At Risk ◽  
Elderly People ◽  
Fall Risk ◽  
Postural Stability ◽  
Domain Knowledge ◽  
The Elderly ◽  
Multiscale Entropy ◽  
Risk Of Falling ◽  
Timed Up And Go ◽  
Not At Risk

As people in developed countries live longer, assessing the fall risk becomes more important. A major contributor to the risk of elderly people falling is postural instability. This study aimed to use the multiscale entropy (MSE) analysis to evaluate postural stability during a timed-up-and-go (TUG) test. This test was deemed a promising method for evaluating fall risk among the elderly in a community. The MSE analysis of postural instability can identify the elderly prone to falling, whereupon early medical rehabilitation can prevent falls. Herein, an objective approach is developed for assessing the postural stability of 85 community-dwelling elderly people (aged 76.12 ± 6.99 years) using the short-form Berg balance scale. Signals were collected from the TUG test using a triaxial accelerometer. A segment-based TUG (sTUG) test was designed, which can be obtained according to domain knowledge, including “Sit-to-Walk (STW),” “Walk,” “Turning,” and “Walk-to-Sit (WTS)” segments. Employing the complexity index (CI) of sTUG can reveal information about the physiological dynamics’ signal for postural stability assessment. Logistic regression was used to assess the fall risk based on significant features of CI related to sTUG. MSE curves for subjects at risk of falling (n = 19) exhibited different trends from those not at risk of falling (n = 66). Additionally, the CI values were lower for subjects at risk of falling than those not at risk of falling. Results show that the area under the curve for predicting fall risk among the elderly subjects with complexity index features from the overall TUG test is 0.797, which improves to 0.853 with the sTUG test. For the elderly living in a community, early assessment of the CI for sTUG using MSE can help predict the fall risk.

Vestibular Function and Motion Sickness Susceptibility: Videonystagmographic Evidence From Oculomotor and Caloric Tests

2020 ◽  
Vol 29 (2) ◽  
pp. 188-198
Author(s):  
Cynthia G. Fowler ◽  
Margaret Dallapiazza ◽  
Kathleen Talbot Hadsell
Keyword(s):  
Phase Velocity ◽  
Motion Sickness ◽  
Repeated Measures ◽  
Short Form ◽  
Vestibular Function ◽  
Slow Phase ◽  
Test Results ◽  
Normal Range ◽  
Slow Phase Velocity ◽  
Caloric Tests

Purpose Motion sickness (MS) is a common condition that affects millions of individuals. Although the condition is common and can be debilitating, little research has focused on the vestibular function associated with susceptibility to MS. One causal theory of MS is an asymmetry of vestibular function within or between ears. The purposes of this study, therefore, were (a) to determine if the vestibular system (oculomotor and caloric tests) in videonystagmography (VNG) is associated with susceptibility to MS and (b) to determine if these tests support the theory of an asymmetry between ears associated with MS susceptibility. Method VNG was used to measure oculomotor and caloric responses. Fifty young adults were recruited; 50 completed the oculomotor tests, and 31 completed the four caloric irrigations. MS susceptibility was evaluated with the Motion Sickness Susceptibility Questionnaire–Short Form; in this study, percent susceptibility ranged from 0% to 100% in the participants. Participants were divided into three susceptibility groups (Low, Mid, and High). Repeated-measures analyses of variance and pairwise comparisons determined significance among the groups on the VNG test results. Results Oculomotor test results revealed no significant differences among the MS susceptibility groups. Caloric stimuli elicited responses that were correlated positively with susceptibility to MS. Slow-phase velocity was slowest in the Low MS group compared to the Mid and High groups. There was no significant asymmetry between ears in any of the groups. Conclusions MS susceptibility was significantly and positively correlated with caloric slow-phase velocity. Although asymmetries between ears are purported to be associated with MS, asymmetries were not evident. Susceptibility to MS may contribute to interindividual variability of caloric responses within the normal range.

scholarly journals Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisha Yu ◽  
Yang Zhao ◽  
Hailiang Wang ◽  
Tien-Lung Sun ◽  
Terrence E. Murphy ◽  
...  
Keyword(s):  
Predictive Models ◽  
Cross Validation ◽  
Motor Coordination ◽  
Predictive Accuracy ◽  
Short Form ◽  
Community Dwelling ◽  
Regularized Regression ◽  
Balance Assessment ◽  
Timed Up And Go ◽  
Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

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