Situation Awareness Inferred from Posture Transition and Location; derived from smart phone and smart home sensors

Abstract

Situation awareness may be inferred from user context such as body posture transition and location data. Smart phones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user’s physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location and heart rate) based on two models: ‘certain fall’ and ‘possible fall’. The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and an RFID network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7% and specificity of 85.7%. By providing appropriate context the robustness of situation recognition algorithms can be enhanced