Future Activities Prediction Framework in Smart Homes Environment

Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL can be a great asset while analyzing residents’ behavior to provide residents with better and optimized services. A popular example is analyze predict future activities optimize smart performance accordingly. This paper proposes forecasting framework that utilizes next in home environment. Forecasting performed via the conjunction embedding algorithm encode Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks process data. The proposed evaluated over five real datasets where experiments show outperformance accuracy scores ranging from 98.7% 93.8%.