Sensor-based Fall Risk Assessment – an Expert ‘to go’

Marschollek, Michael; Rehwald, Anja; Wolf, Klaus-Hendrik; Gietzelt, Matthias; Nemitz, G.; Schwabedissen, H. Meyer zu; Haux, Reinhold

doi:10.3414/me10-01-0040

Cited by 78 publications

(36 citation statements)

References 45 publications

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“…The minimum acceleration peak right when the acceleration reaches steady state Marschollek et al [15] investigated the use of wearable accelerometers to provide objective data on motion features and automatically assessed individual fall risk. A sensor-based assessment of fall risk was performed, which employs gait and motion parameters obtained during a TUG test and a 20 m walk.…”

Section: Features Extracted From Accelerometersmentioning

confidence: 99%

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Lee

Sun

Jiang

et al. 2016

Entropy

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Abstract:In this paper, the Multiscale Entropy (MSE) analysis of acceleration data collected from a wearable inertial sensor was compared with other features reported in the literature to observe falling behavior from the acceleration data, and traditional clinical scales to evaluate falling behavior. We use a fall risk assessment over a four-month period to examine >65 year old participants in a community service context using simple clinical tests, including the Short Form Berg Balance Scale (SFBBS), Timed Up and Go test (TUG), and the Short Portable Mental Status Questionnaire (SPMSQ), with wearable accelerometers for the TUG test. We classified participants into fallers and non-fallers to (1) compare the features extracted from the accelerometers and (2) categorize fall risk using statistics from TUG test results. Combined, TUG and SFBBS results revealed defining features were test time, Slope(A) and slope(B) in Sit(A)-to-stand(B), and range(A) and slope(B) in Stand(B)-to-sit(A). Of (1) SPMSQ; (2) TUG and SPMSQ; and (3) BBS and SPMSQ results, only range(A) in Stand(B)-to-sit(A) was a defining feature. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and the combined TUG and SFBBS are all distinguishable, showing that MSE can effectively classify participants in these clinical tests using behavioral actions. This study highlights the advantages of body-worn sensors as ordinary and low cost tools available outside the laboratory. The results indicated that MSE analysis of acceleration data can be used as an effective metric to categorize falling behavior of community-dwelling elderly. In addition to clinical application, (1) our approach requires no expert physical therapist, nurse, or doctor for evaluations and (2) fallers can be categorized irrespective of the critical value from clinical tests.

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Section: Features Extracted From Accelerometersmentioning

confidence: 99%

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Lee

Sun

Jiang

et al. 2016

Entropy

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“…The use of inertial sensors to help clinicians to assess the risk of fall has been widely studied [9], [22]- [24]. These systems generally derive a model to classify users as fallers or non-fallers, on the basis of some relevant gait parameters.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

An On-Node Processing Approach for Anomaly Detection in Gait

et al. 2015

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Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The user's gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real-time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ∼ 84% accuracy in the recognition of abnormal gait segments lasting ∼ 5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

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“…However, these can be subjective, variable in administration and may require specialist expertise. As a result, quantitative methods for estimating falls risk have been investigated [11,12,13,14]. An objective method for assessing falls risk, suitable both for use by non-experts and for deployment in a community care setting, may find clinical application for screening and targeting of individuals at high risk.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors

et al. 2012

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Background: Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. This study aimed to determine if a method based on body-worn sensor data can prospectively predict falls in community-dwelling older adults, and to compare its falls prediction performance to two standard methods on the same data set. Methods: Data were acquired using body-worn sensors, mounted on the left and right shanks, from 226 community-dwelling older adults (mean age 71.5 ± 6.7 years, 164 female) to quantify gait and lower limb movement while performing the ‘Timed Up and Go’ (TUG) test in a geriatric research clinic. Participants were contacted by telephone 2 years following their initial assessment to determine if they had fallen. These outcome data were used to create statistical models to predict falls. Results: Results obtained through cross-validation yielded a mean classification accuracy of 79.69% (mean 95% CI: 77.09–82.34) in prospectively identifying participants that fell during the follow-up period. Results were significantly (p < 0.0001) more accurate than those obtained for falls risk estimation using two standard measures of falls risk (manually timed TUG and the Berg balance score, which yielded mean classification accuracies of 59.43% (95% CI: 58.07–60.84) and 64.30% (95% CI: 62.56–66.09), respectively). Conclusion: Results suggest that the quantification of movement during the TUG test using body-worn sensors could lead to a robust method for assessing future falls risk.

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Sensor-based Fall Risk Assessment – an Expert ‘to go’

Cited by 78 publications

References 45 publications

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

An On-Node Processing Approach for Anomaly Detection in Gait

Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors

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