A Framework for Utilizing Serious Games and Machine Learning to Classifying Game Play Towards Detecting Cognitive Impairments

Objective: This work presents a framework for geriatricians with interests in exploring the opportunities that serious games and machine learning may offer in assisting with diagnoses of cognitive impairment. Mild Cognitive Impairment (MCI) is often a precursor to more serious dementias, and this work focuses on the potential of applying machine learning (ML) approaches to data from serious games to detect MCI. The focus on MCI detection sets the work apart from mobile games marketed as ‘brain training’ to maintain mental acuity.

Materials & methods: Our prototypical game is denoted WarCAT. WarCAT captures players’ moves during the game to infer processes of strategy recognition, learning, and maintenance (memory, or conversely, loss). As with any ML endeavor, effectively demonstrating this conjecture requires large datasets, and thus, the objective of this work is to also develop ML methods to generate synthetic data that can plausibly emulate a large population of players. To generate synthetic data, the ML paradigm of Reinforcement Learning (RL) is being applied as it most closely emulates the way humans learn. Subsequently, The ML-based classification of game-play data to identify varying degrees of cognitive impairment.

Results: We have developed RL bots that process millions of gameplay training patterns and achieve gameplay results comparable to the best human performance. This baseline allows us to create bots with various levels of training to emulate individuals at various stages of learning, or by extension, various levels of cognitive decline.

Conclusion: The work introduces a framework to demonstrate the characteristics and potential that ML offers to cognitive health diagnosis. Specifically, the work demonstrates the RL work and the ML methods to successfully classify different levels of gameplay.

Keywords: Mild cognitive impairment; Machine learning; Reinforcement learning; Serious games