|Title||Automated fall detection with quality improvement "rewind" to reduce falls in hospital rooms|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Marilyn Rantz, Tanvi Banerjee, Erin Cattoor, Susan Scott, Mihail Popescu, Marjorie Skubic|
|Journal||Journal of Gerontological Nursing Technology Innovations|
The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy.