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Falls are a major problem in older adults worldwide with an estimated 30% of elderly adults over 65 years of age falling each year. The direct and indirect societal costs associated with falls are enormous. A system that could provide an accurate automated assessment of falls risk prior to falling would allow timely intervention and ease the burden on overstretched healthcare systems worldwide. An objective method for assessing falls risk using body-worn kinematic sensors is reported. The gait and balance of 349 community-dwelling elderly adults was assessed using body-worn sensors while each patient performed the "timed up and go" (TUG) test. Patients were also evaluated using the Berg balance scale (BBS). Of the 44 reported parameters derived from body-worn kinematic sensors, 29 provided significant discrimination between patients with a history of falls and those without. Cross-validated estimates of retrospective falls prediction performance using logistic regression models yielded a mean sensitivity of 77.3% and a mean specificity of 75.9%. This compares favorably to the cross-validated performance of logistic regression models based on the time taken to complete the TUG test (manually timed TUG) and the Berg balance score. These models yielded mean sensitivities of 58.0% and 57.8%, respectively, and mean specificities of 64.8% and 64.2%, respectively. Results suggest that this method offers an improvement over two standard falls risk assessments (TUG and BBS) and may have potential for use in supervised assessment of falls risk as part of a longitudinal monitoring protocol.

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Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

Frailty is an important geriatric syndrome strongly linked to falls risk as well as increased mortality and morbidity. Taken alone, falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Reliable determination of older adults' frailty state in concert with their falls risk could lead to targeted intervention and improved quality of care. We report a mobile assessment platform employing inertial and pressure sensors to quantify the balance and mobility of older adults using three physical assessments (timed up and go (TUG), five times sit to stand (FTSS) and quiet standing balance). This study examines the utility of each individual assessment, and the novel combination of assessments, to screen for frailty and falls risk in older adults.Data were acquired from inertial and pressure sensors during TUG, FTSS and balance assessments using a touchscreen mobile device, from 124 community dwelling older adults (mean age 75.9 ± 6.6 years, 91 female). Participants were given a comprehensive geriatric assessment which included questions on falls and frailty. Methods based on support vector machines (SVM) were developed using sensor-derived features from each physical assessment to classify patients at risk of falls risk and frailty.In classifying falls history, combining sensor data from the TUG, Balance and FTSS tests to a single classifier model per gender yielded mean cross-validated classification accuracy of 87.58% (95% CI: 84.47-91.03%) for the male model and 78.11% (95% CI: 75.38-81.10%) for the female model. These results compared well or exceeded those for classifier models for each test taken individually. Similarly, when classifying frailty status, combining sensor data from the TUG, balance and FTSS tests to a single classifier model per gender, yielded mean cross-validated classification accuracy of 93.94% (95% CI: 91.16-96.51%) for the male model and 84.14% (95% CI: 82.11-86.33%) for the female model (mean 89.04%) which compared well or exceeded results for physical tests taken individually.Results suggest that the combination of these three tests, quantified using body-worn inertial sensors, could lead to improved methods for assessing frailty and falls risk.

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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.