Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training
This addresses engagement and skill transfer issues in cognitive training for older adults, but it is incremental as it combines existing technologies like VR and EMG in a new application.
The paper tackled low engagement and poor generalization in cognitive training by developing an adaptive VR game that uses facial EMG to detect user affect and adjust difficulty in real-time, achieving affect recognition rates of 64.1% for valence and 76.2% for arousal and showing that adaptation leads to greater feelings of competence in older adults.
Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills.