ENSEMBLE HUMAN MOVEMENT SEQUENCE PREDICTION MODEL USING APRIORI AND BAGGED J48 ON MACHINE LEARNING

The accurate prediction of human movement trajectory has a variety of benefits for many applications such as optimizing day-today home movements of old or disabled people to minimize their routine efforts, etc. In order to perform human movement prediction, large amount of historical positioning data from sensors has to be collected and mined by analyzing different human sequential movement prediction approaches and their limitations. In this proposed work, Apriori algorithm which predicts the human movement sequence patterns in indoor environment. The Apriori is integrated into Bagged J48 Machine learning algorithm which results in an ensemble model to predict the future human movement patterns. These patterns are mined based on spatial, temporal and social data which add more accuracy to our prediction. This model also performs clustering mechanism to detect the abnormal patterns.