Fall Detection for Elderly and Patient

Falls cause injury and death for elderly and patients, which account for up to 70% of accidents. The risk of falls increases markedly with age, slower reaction and balance, reduced muscle strength.

The technology developed segments daily human activities into a set of pre-defined activities which then a quantitative analysis of each activity at its different stages is performed. The system promptly detects for the distinctive characteristics of falls and immediately triggers an alert to the relevant parties.

Human Activity Modelling, which is a technology developed to intepret human activities and thus able to detect and send out warnings for particular actions of interest.

The technology is suitable for enviroments such as nursing homes, retirement homes, hospitals and homes with elderly residents to trigger for assistance quickly after a fall. Other potential applications can be as follows: Automatic learning of Activities in Daily Living (ADL,) Abnormal behaviour detection, Tagging contents on visual data.