Situation awareness is a must for a smart home to exhibit its smartness. Normally, this is accomplished by accurately detecting the activities of a home user and then responding to the need of the user accordingly. This research utilizes a single wearable device equipped with an accelerometer and a gyroscope to detect eight potential activities in the living room of a smart home environment. First, the models of activities are constructed based on training data generated from the wearable device. Then, when a user performs the activity, the newly generated data would be compared with the established models to identify the type of current activity. Our method of model construction and activity detection is based on Decision Tree and Hidden Markov Model (HMM) with the assistance of location data derived from Beacons. The unique advantage of our method lies in its low cost as only one wearable device and a couple of beacons are required for achieving the desired situation awareness.