Functional activity monitoring from wearable sensor data.

A novel approach is presented for the interpretation and use of EMG and accelerometer data to monitor, identify, and categorize functional motor activities in individuals whose movements are unscripted, unrestrained, and take place in the "real world". Our proposed solution provides a novel and practical way of conceptualizing physical activities that facilitates the deployment of modern signal processing and interpretation techniques to carry out activity monitoring. A hierarchical approach is adopted that is based upon: 1) blackboard and rule-based technology from artificial intelligence to support a process in which coarse-grained activity partitioning forms the context for finer-grained activity partitioning; 2) neural network technology to support initial activity classification; and 3) integrated processing and understanding of signals (IPUS) technology for revising the initial classifications to account for the high degrees of anticipated signal variability and overlap during freeform activity.