| Summary: | We describe a distributed architecture for a real-time falls prevention framework capable of providing a technological intervention to mitigate the risk of falls in acute hospitals through the development of an AmbIGeM (Ambient Intelligence Geritatric Management system). Our approach is based on using a battery free, wearable sensor enabled Radio Frequency Identification device. Unsupervised classification of high risk falls activities are used to facilitate an immediate response from caregivers by alerting them of the high risk activity, the particular patient, and their location. Early identification of high risk falls activities through a longitudinal and unsupervised setting in real-time allows the preventative intervention to be administered in a timely manner. Furthermore, real-time detection allows emergency protocols to be deployed immediately in the event of a fall. Finally, incidents of high risk activities are automatically documented to allow clinicians to customize and optimize the delivery of care to suit the needs of patients identified as being at most risk.
|
|---|