Research研究方向
Multimodal affective computing, ubiquitous psychological computing, edge and embodied intelligence, and cognitive human factors engineering.围绕多模态情感计算、普适心理计算、端侧与具身情感智能、认知与人因工程，连接机理、设备与真实场景。
Affective computing and human factors情感计算与人因工程
MAC-Lab retains its established brand while adopting the full name Multimodal Affective Computing and Human Factors Engineering Laboratory. Our research links human-state understanding, edge and embodied systems, and performance in real tasks.实验室沿用 MAC-Lab 品牌，全称更新为“多模态情感计算与人因工程实验室”。研究连接人的状态理解、端侧与具身系统，以及真实任务中的表现与协作。
A Route That Keeps the Same Center一条保持中心的问题路线
The lab's route grows from Professor Sun's doctoral training at Dalian University of Technology and Tokushima University, and from years of work in natural language processing, human-computer interaction, and multimodal affective computing. Large models, digital humans, smart cockpits, and embodied agents are new carriers for the same long-term question: how can AI better understand and support people?实验室的研究路线，来自孙晓教授在大连理工大学和日本国立德岛大学的中日双博士训练，也来自团队多年在自然语言处理、人机交互和多模态情感计算方向的积累。大模型、数字人、智能座舱和具身智能，都是同一个长期问题的新载体：AI 如何更好地理解和支持人。
Ubiquitous Psychological Computing普适心理计算框架
This framework extends affective computing from isolated emotion recognition to continuous psychological modeling in pervasive scenarios. It treats emotion as a dynamic process shaped by intensity, time, environment, personality, physiological-behavioral signals, and group context, enabling assessment, early warning, and personalized intervention.该框架把情感计算从孤立的情绪识别推进到普适场景中的连续心理建模：从情绪反应程度、时间、环境、个性、生理行为信号和群体情境等维度理解复杂情感变化，支撑心理评估、风险预警和个性化干预。
Embodied Emotional Intelligence具身情感智能框架
This framework brings affective computing into embodied agents. It connects multimodal emotional perception, affective understanding, expressive generation, and situated interaction so that robots, digital humans, smart cockpits, eldercare companions, and active-health systems can move from task execution to trusted emotional interaction.该框架把情感计算带入具身智能体：连接多模态情感感知、情感理解、情感表达生成与情境化交互，让机器人、数字人、智能座舱、养老陪伴和主动健康系统从“能执行任务”走向“能建立可信情感互动”。
Affective NLP and Emotional Dialogue语言智能与情感对话
We develop models for Chinese language understanding, affective semantics, emotional conversation generation, empathetic response, personality-aware dialogue, and LLM-based emotional support. The goal is to help AI read emotional intent, social relationship, and psychological need behind the words.研究中文语言理解、情感语义、人格建模、情感支持对话和多智能体协作，让大模型读懂话语背后的情绪意图、社会关系和心理需求。
Multimodal Affective Computing多模态情绪与心理状态建模
We integrate text, speech, facial expression, micro-expression, gait, posture, eye movement, physiological signals, and environmental context. Research topics include complex emotion recognition, humor detection, stress estimation, depression and anxiety detection, cognitive load recognition, and temporal affect dynamics.融合文本、语音、面部表情、微表情、姿态、步态、眼动和生理信号，构建面向复杂情绪、心理压力、认知负荷和抑郁焦虑的识别模型。
Ubiquitous Psychological Computing普适心理计算
We propose and advance ubiquitous psychological computing: a framework for sensing, profiling, assessing, and supporting mental states in everyday scenarios. This direction emphasizes non-contact sensing, dynamic psychological modeling, long-term state tracking, early warning, and human-centered intervention.把心理状态感知从实验室带到真实空间，面向家庭、学校、医院、座舱和特种作业环境，形成非接触评测、动态画像、风险预警与干预闭环。
Embodied Emotional Intelligence具身情感智能
We study emotional interaction models and embodied agent architectures that act as an emotional brain for physical robots, digital humans, smart devices, and human-machine coexistence environments.研究具身情感交互大模型和智能体架构，为机器人、数字人、智能终端和人机共生环境提供可部署的“情感大脑”。
Cognitive & human factors engineering认知与人因工程
We study the interaction between people, tasks, equipment, and environments: how workload, fatigue, stress, and trust relate to performance and collaboration. Research questions include individual differences, changes across tasks, and the evaluation of assistance strategies.围绕人、任务、装备与环境的相互作用，研究认知负荷、疲劳、压力与信任如何关联任务表现和协作过程，关注个体差异、跨任务变化及辅助策略的效果评价。
The agenda includes civilian and defense-related personnel protection and training evaluation. Proposed capabilities are research objectives; evidence and deployment scope must be assessed for each project.研究面向民用与国防相关的人员保障、训练评估和人机协同。拟突破能力作为研究目标呈现，实验结果与实际应用范围按项目分别说明。
From models to devices从模型走向设备
Moving models onto hardware raises new research questions: synchronization and missing signals, resource-constrained inference, long-term memory, and adaptation during interaction. We evaluate latency, reliability, cross-context generalization, and effects on the human task.模型进入硬件之后，新的问题包括多源信号同步与缺失、资源约束下的推理、长期记忆和交互适应。围绕时延、可靠性、跨场景泛化及对人的任务影响开展评价。
研究积累与建设历程Research foundations and lab history
这些方向延续了团队在语言理解、情感计算与系统应用上的长期积累。孙晓与任福继共同推进合工大情感计算的建设历史、各阶段成果及公开资料，集中收录在建设脉络页。These directions build on the team's work in language understanding, affective computing, and applied systems. The lab history records Xiao Sun and Fuji Ren's shared work at HFUT, milestones, and public references.
查看完整时间线与资料来源 →Explore the full timeline and sources →