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Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Activity recognition is the problem of predicting the movement of a person, often indoors, based on sensor data, such as an accelerometer in a smartphone. Streams of sensor data are often split into subs-sequences called windows, and each window is associated with a broader activity, called a sliding window approach. Recognition of human activities aims a wide diversity of applications. Recognizing human activities by means of sensors attached on the body has been widely studied. common activity and functional performance level of a person can be determined by the capability to record and identify distinctive daily activities.Security and entertainment are others ﬁelds impacted by investigation of human behaviour through mobile phone data.Motion capture video systems have become an important research topic in the monitor human activity. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent as needed.