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, twodimensional (2D) materials are employed to construct an ecofriendly 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 shortterm memory circuit, normalisation circuit module, and multilayer 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.

We present a circuit design of the hierarchical attention network for multimodal affective computing, which can be used in mental health monitoring. Specifically, a kind of cost-effective memristor is fabricated using the albumen protein, and the corresponding testing performance is conducted to ensure its efficiency and stability. Then, considering the hierarchical mechanism inspired by the human limbic system, the nanoscale memristors arranged in a crossbar array configuration are further applied to construct a compact hierarchical attention network that can perform the multimodal affective computing. Furthermore, based on the wearable technology and flexible electronics technology, a mental health monitoring system with low privacy invasiveness, low energy consumption, and low fabrication cost can be designed. Based on the mapping relationship between the multimodal affective computing and mental health, the mental health state of the current user can be monitored. This study is expected to help achieving the deep integration of neuromorphic electronics and mental health monitoring system, further promoting the development of next-generation consumer healthcare technology in smart city.

Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer from intensive computation and low efficiency, due to the von Neumann computing architecture. Here, we present a brain-inspired hierarchical interactive in-memory computing (IMC) system, which can efficiently solve 'von Neumann bottleneck', enabling cross-modal interactions and semantic gap elimination. First, a 1T1M synapse array is fabricated using cost-effective, highly stable, flexible, and eco-friendly carbon materials, offering efficient analog multiply-accumulate operations. To illustrate the complexity of the proposed brain-inspired hierarchical interactive IMC system, three modules are proposed: 1) unimodal extraction module, 2) hierarchical interactive module, 3) output module. Furthermore, the proposed system is validated by applying it to video sentiment analysis. The experimental results demonstrate that the proposed system outperforms the existing state-of-the-art methods with high computational efficiency and good robustness. This work opens up a new way to achieve the deep integration of nanomaterials, deep learning, and modern electronics into IMC.

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