Abstract: Methods and systems may provide for estimating falls risk based on inertial sensor data collected during a Five Times Sit-to-Stand (FTSS) test. In an embodiment, a classifier model may be trained with inertial sensor data collected from a sample of people performing the FTSS test and their self-reported falls history. In an embodiment, one or more features related to steadiness or smoothness of the person's movement may be calculated. In an embodiment, one or more features related to timing of the FTSS test, such as a total time taken to complete the FTSS test or to complete individual sit-stand-sit (SSS) phases of the test, may be calculated. In an embodiment, supervised pattern recognition techniques may train the classifier model to classify a person as being likely to fall or not being likely to fall based on FTSS-related feature values collected from that person.

Abstract: Methods and systems may provide for estimating falls risk based on inertial sensor data collected during a Five Times Sit-to-Stand (FTSS) test. In an embodiment, a classifier model may be trained with inertial sensor data collected from a sample of people performing the FTSS test and their self-reported falls history. In an embodiment, one or more features related to steadiness or smoothness of the person's movement may be calculated. In an embodiment, one or more features related to timing of the FTSS test, such as a total time taken to complete the FTSS test or to complete individual sit-stand-sit (SSS) phases of the test, may be calculated. In an embodiment, supervised pattern recognition techniques may train the classifier model to classify a person as being likely to fall or not being likely to fall based on FTSS-related feature values collected from that person.

Abstract: Methods and systems may provide for falls risk assessment using body-worn sensors. If executed by the processor, the instructions can cause the system to calculate a timed up and go (TUG) time segment based on angular velocity data from the plurality of kinematic sensors. The instructions may also cause the system to calculate one or more derived parameters based on the angular velocity data, including temporal gait parameters, spatial gait parameters, tri-axial angular velocity parameters, and turn parameters. Falls data may be collected retrospectively, based on whether the test participant has fallen in the past. Falls data may be collected prospectively, in which the individual is contacted in the future to determine if they have fallen. This outcome data may be used to train regularized discriminant classifier models based on relevant sub-sets of the feature set, selected using sequential forward feature selection.