Machine learning and knowledge engineering for cognitive memory assessment of age groups by anomalies in a serious game

In psychology, clinical instruments are often used for assessing the cognitive domains and detecting mental alterations. Nevertheless, after the screening stage, the recommendation of activities for training or new assessments could take a long time. Other instruments such digital serious games are commonly used to train cognitive domains, unlike classical instruments, can collect extra data on players' behavior without altering a person's state. In the other hand, decision-making tools are based mainly on two techniques, machine learning for revealing hidden data patterns, and knowledge engineering for building knowledge-based systems. However, the synergy among these both techniques is not completely leveraged, otherwise, when it is, this intelligent data analysis allows the creation of hypotheses and validate contradictions. In this context, this paper presents a tool for supporting the cognitive memory assessment of people based on machine learning and knowledge engineering (M&K-CogMem). The source of data is a matching game for memory training, and after that, this tool determines the memory status of players among their respective age groups, it based on the scores and times in the game. In case of detecting a cognitive memory alteration, it recommends activities for memory improvement through its inference engine. Testing its adoption in industry, an empirical evaluation carried out by psychologists through a technology acceptance model showed positive indicators.