Fast matching of sensor data with manual observations

In systems and trials concerning wearable sensors and devices used for medical data collection, the validation of sensor data with respect to manual observations is very important. However, this is often problematic because of feigned behavior, errors in manual recording (misclassification), gaps in recording (missing readings), missed observations and timing mismatch between manual observations and sensor data due to a difference in time granularity. Using sleep activity pattern monitoring as an example we present a fast algorithm for matching sensor data with manual observations. Major components include a) signal analysis to classify states of sleep activity pattern, b) matching of states with Sleep Diary (SD) and c) automated detection of anomalies and reconciliation of mismatches between the SD and the sensor data.