  Smart home technology is a better choice for the people to care about
security, comfort and power saving as well. It is required to develop
technologies that recognize the Activities of Daily Living (ADLs) of the
residents at home and detect the abnormal behavior in the individual's
patterns. Data mining techniques such as Frequent pattern mining (FPM), High
Utility Pattern (HUP) Mining were used to find those activity patterns from the
collected sensor data. But applying the above technique for Activity
Recognition from the temporal sensor data stream is highly complex and
challenging task. So, a new approach is proposed for activity recognition from
sensor data stream which is achieved by constructing Frequent Pattern Stream
tree (FPS - tree). FPS is a sliding window based approach to discover the
recent activity patterns over time from data streams. The proposed work aims at
identifying the frequent pattern of the user from the sensor data streams which
are later modeled for activity recognition. The proposed FPM algorithm uses a
data structure called Linked Sensor Data Stream (LSDS) for storing the sensor
data stream information which increases the efficiency of frequent pattern
mining algorithm through both space and time. The experimental results show the
efficiency of the proposed algorithm and this FPM is further extended for
applying for power efficiency using HUP to detect the high usage of power
consumption of residents at smart home.
