Real-time unusual user event detection algorithm fusing vision, audio, activity, and dust patterns

According to the statistics examined by the National Safety Council, Injury Facts in 2017, a significant number of preventable injury-related deaths occurred in home and indoor public areas, and the rate of preventable deaths occurring indoors has increased by 156% since 1999. Indoor smart Internet of Things devices such as security cameras, intelligent speakers, smartphones, and air cleaners are being utilized to seek help during dangerous or emergency situations in indoor settings. Theses Internet of Things devices can also assist single-person household rescues during emergency situations where no one is present for assistance. We propose a real-time algorithm to detect unusual user events which may help in reducing the rate of people dying alone and remaining undiscovered for a long period of time. We designed and developed unusual user behavior patterns related to vision, audio, dust, and activity via Internet of Things sensors and fused these patterns to improve the performance accuracy. We evaluated the proposed individual pattern algorithms and the fusion method through the data collected in indoor smart environments.