Current context-aware applications often use thelocation of a user as the only indication of the current situation.These existing applications are therefore limited in their situationawareness, because of the poor indoor resolution of the locationsensor and its high resource consumption. In response to theselimitations we present an approach to estimate the contextualsituation of a user without using resource inefficient locationsensors. Our proposed solution utilizes a wide range of lowpowered sensors, together with two modified machine learningtechniques to estimate the situation in a more resource efficientmanner. Simulations and a proof-of-concept application show thatthe situation of a user can be determined within 50 ms at anaccuracy above 90%, when only using the low energy sensorsavailable on a smartphone and its limited processing power.