Applying new sensing technology to healthcare maybe part of a solution to the financial and demographic crisis facing global healthcare systems. Researchers applying new approaches to noninvasive patient monitoring and diagnostics are assisted by the features of Sensing Health with Intelligence, Modularity, Mobility and Experimental Reusability (SHIMMER™), a flexible sensing platform. Integrated peripherals, open software, modular expansion, specific power management hardware, and a library of applications supported with platform validation provide SHIMMER with advantages over many other medical research platforms.

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.

The five-times-sit-to-stand test (FTSS) is an established assessment of lower limb strength, balance dysfunction and falls risk. Clinically, the time taken to complete the task is recorded with longer times indicating increased falls risk. Quantifying the movement using tri-axial accelerometers may provide a more objective and potentially more accurate falls risk estimate. 39 older adults, 19 with a history of falls, performed four repetitions of the FTSS in their homes. A tri-axial accelerometer was attached to the lateral thigh and used to identify each sit-stand-sit phase and sit-stand and stand-sit transitions. A second tri-axial accelerometer, attached to the sternum, captured torso acceleration. The mean and variation of the root-mean-squared amplitude, jerk and spectral edge frequency of the acceleration during each section of the assessment were examined. The test-retest reliability of each feature was examined using intra-class correlation analysis, ICC(2,k). A model was developed to classify participants according to falls status. Only features with ICC>0.7 were considered during feature selection. Sequential forward feature selection within leave-one-out cross-validation resulted in a model including four reliable accelerometer-derived features, providing 74.4% classification accuracy, 80.0% specificity and 68.7% sensitivity. An alternative model using FTSS time alone resulted in significantly reduced classification performance. Results suggest that the described methodology could provide a robust and accurate falls risk assessment.