Associative memory and recall model with KID model for human activity recognition

Abstract

Learning, memorizing and recalling knowledge are the basic functions of cognitive models. These models must prioritize which stimulants to respond to as well as package acquired knowledge in an easy to retrieve manner. The human brain is a cognitive model that derives information from sensor data such as vision, associates different patterns to create knowledge, and uses chunking mechanisms to package the acquired knowledge in manageable entities. The use of chunking mechanisms by the brain aids it to overcome its short-term memory (STM) capacity limitation. Through chunking, each entity held in the STM is a chunk containing more associations (knowledge) in it. By mimicking the human brain, this study proposes an associative memory and recall (AMR) model that stores associative knowledge from sensor data. Using chunking mechanisms, AMR can organize human activity knowledge in the manner that is efficient and effective to store and recall. The knowledge–information–data (KID) model is used for learning associative knowledge while the AMR continuously looks for associations among knowledge units and merges related units using merging mechanisms. The chunking mechanisms used in this study are inspired by the chunking mechanisms of the brain i.e. goal oriented chunking and automatic chunking.