METHOD AND SYSTEM FOR PREDICTION OF CORRECT DISCRETE SENSOR DATA BASED ON TEMPORAL UNCERTAINTY

摘要

This disclosure relates generally to a method and system for prediction of correct discrete sensor data, thus enabling continuous flow of data even when a discrete sensor fails. The activities of humans/subjects, housed in a smart environment is continuously monitored by plurality of non-intrusive discrete sensors embedded in living infrastructure. The collected discrete sensor data is usually sparse and largely unbalanced, wherein most of the discrete sensor data is 'No' and comparatively only a few samples of 'Yes', hence making prediction very challenging. The proposed prediction techniques based on introduction of temporal uncertainty is performed in several stages which includes preprocessing of received discrete sensor data, introduction of temporal uncertainty techniques followed by prediction based on neural network techniques of learning pattern using historical data.