EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

Abstract

Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, two-dimensional (2D) materials are employed to construct an eco-friendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long short-term memory circuit, normalisation circuit module, and multi-layer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.