To address the mental health needs of groups such as left-behind children, long-term hospitalized patients, and the elderly living alone, as well as the limitations of traditional psychological services including uneven resource distribution and delayed response, an AI emotional interaction and psychological assessment companion robot based on an embedded platform was designed. The robot adopts a "local terminal+cloud/PC+mobile APP" collaborative architecture, with an embedded AI large model as its core, integrating multimodal perception, real-time psychological assessment, and autonomous mobility functions. At the hardware level, hierarchical design enables friendly interaction and flexible movement. At the software level, a "perception-assessment-feedback" closed-loop system is constructed, featuring core functions such as voice interaction, emotion recognition, dynamic psychological assessment, and visual report generation. Test results show that the robot achieves a speech recognition accuracy ≥96%, an emotion recognition accuracy ≥92%, and a system response time ≤0.8 seconds. It can provide continuous psychological support in various scenarios such as schools, medical institutions, and homes, effectively alleviating the psychological distress of target groups and offering an intelligent solution for mental health management.

Luo Shunan, Sun Xiaoqing, Zhang Yucheng, Deng Yun. Design of a Multimodal Psychological Companion Robot Based on Embedded AI. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 5: 64-69. https://doi.org/10.25236/AJETS.2025.080509.

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