| Hi-index | 0.00 |
Older adults suffering from Alzheimer's disease often require assistance with performing simple activities of daily living, such as washing their hands in the bathroom. This severely limits their independence and places a heavy care giving burden on their family and the healthcare system. The motivation for developing a water detection algorithm is for it to be used within a system that provides reminding prompts for Alzheimer's sufferers and to study product usability for older adults with cognitive impairments. Water detection in a video sequence poses a challenging computer vision problem since it is difficult to model the flow of water in a structured manner. A real-time detection system is presented here that estimates the presence of flowing water in a bathroom sink during a hand washing task based on classifying video and audio features with an overall accuracy of 88.76%. Visual features are extracted using temporal image derivatives and hand tracking is used to enhance the robustness in the visual features.