Cases能力案例
Representative capability cases showing how MAC-Lab turns affective computing, psychological computing, and embodied emotional intelligence into deployable systems.展示 MAC-Lab 如何把情感计算、心理计算与具身情感智能转化为可部署系统的代表性能力案例。
From Research Route to Deployable Capability从研究路线到可交付能力
These cases help partners read MAC-Lab's capability map: what kinds of human-state problems we work on, how we organize sensing, modeling, evaluation, and support, and where a research idea can become a field-ready system.这些案例帮助合作伙伴理解 MAC-Lab 的能力图谱：我们面向哪些人状态问题，如何组织感知、建模、评价与支持，以及一个研究想法如何走向可在场景中验证的系统。
Representative Capability Cases代表性能力案例
Mindmirror / SoulMirror
Face-to-face multimodal psychological assessment for scenes that need evidence, interpretation, and a human-centered review process.面向需要证据、解释和人在回路复核的面对面多模态心理评估场景。
Mindmirror connects multimodal interaction, guided dialogue, scale-informed profiling, and report-level interpretation into a reflective assessment experience. Body, voice, expression, posture, and dialogue converge as evidence for a respectful and interpretable assessment route.Mindmirror 把多模态交互、引导式对谈、量表启发画像和报告级解释组织成一种可复盘的评估体验。身体、语音、表情、姿态和对话共同构成证据，汇聚成一条尊重用户、可解释的评估路线。
- Capability shown展示能力
- multimodal assessment, camera and voice interaction, psychological profiling, reflective dialogue, report-level interpretation多模态评估、摄像头与语音交互、心理画像、反思式对谈、报告级解释
MindScore
Organizational mind-body growth support for continuous care, group insight, and intervention coordination.面向组织持续关怀、群体态势感知和干预协同的心身成长支持能力。
MindScore extends assessment into real organizational settings. It links long-term psychological archives, non-contact multimodal sensing, baseline comparison, trend awareness, and workflow coordination, helping institutions move from observed evidence to growth support and early awareness.MindScore 将评估能力延伸到真实组织场景，把长期心理档案、非接触多模态感知、个体与群体基线、趋势感知和干预流程连接起来，帮助机构建立从证据观察到成长支持和早期感知的负责路线。
Guardian心身成长
守护
- Capability shown展示能力
- mind-body assessment, longitudinal profiles, baseline comparison, early awareness, intervention coordination身心评估、长期档案、基线对比、早期感知、干预协同
MindOS
A public-facing route for affective mind computing, psychological digital twins, and long-term embodied agents.面向情感心智计算、心理数字孪生和长期具身智能体的公开能力路线。
MindOS is the lab's affective mind-computing foundation. It supports agents that need state continuity, persona stability, embodied response, and individual-to-group scenario reasoning. Fluent language becomes more valuable when it is grounded in a stable mind-computing backbone.MindOS 是实验室的情感心智计算底座，支撑状态连续性、人格稳定性、具身回应和个体到群体场景推演。流畅语言只有接入稳定的心智计算骨架，才能形成更可靠的情感智能。
- Capability shown展示能力
- affective mind modeling, embodied intelligence, psychological digital twins, long-term agent evolution, scenario reasoning情感心智建模、具身智能、心理数字孪生、长期智能体演化、场景推演
MindTalk
Simulation and training for counseling, supervision, group emotion, youth-support, and strategy exploration.面向咨询、督导、群体情绪、青少年支持和策略探索的推演训练能力。
MindTalk moves from single-session dialogue toward multi-domain psychological simulation and training. It places emotional AI inside scenario systems where roles, pressure, group dynamics, intervention choices, and evaluation evidence can be reviewed together.MindTalk 从单轮或单次对话走向多域心理仿真与训练，把情感 AI 放进包含角色、压力、群体动力、干预选择和评估证据的场景系统中检验。
- Capability shown展示能力
- psychological simulation, counseling and training scenarios, group affect propagation, intervention strategy evaluation, research dashboards心理仿真、咨询与训练场景、群体情绪传播、干预策略评估、研究看板
MindPet
Embodied companion intelligence with affective memory, personality-aware interaction, and situated expression.融合情感记忆、人格化交互和场景化表达的具身陪伴智能。
MindPet explores how companion systems can stay coherent over time. Durable trust depends on continuity, restraint, relationship awareness, and multimodal expression that fits the situation.MindPet 探索长期陪伴系统如何保持连贯。长期信任依赖连续性、克制感、关系感知和符合情境的多模态表达。
Agent陪伴
智能体 Expression表达 Safety安全
- Capability shown展示能力
- companion agents, affective memory, personality-aware interaction, group affect, embodied expression陪伴智能体、情感记忆、人格化交互、群体情绪、具身表达
MemOS-Mind
An affective memory layer for long-term AI interaction, service continuity, and psychologically aware response.面向长期 AI 交互、服务连续性和心理感知回应的情感记忆层。
MemOS-Mind makes the memory question sharper: a system records what happened, models what the event meant to the user, and responds through a stable persona and emotional stance. This supports companion AI, learning support, service assistants, and psychologically aware interaction.MemOS-Mind 把记忆问题说得更清楚：系统记录发生了什么，理解这件事对用户意味着什么，并以稳定人格和情感立场回应。这一能力支撑陪伴 AI、学习支持、服务助手和心理感知交互。
- Capability shown展示能力
- affective memory, user-state modeling, persona-consistent response, long-term interaction continuity情感记忆、用户状态建模、人格一致回应、长期交互连续性
From Capability Pattern to Partner Solution从能力案例到合作方案
Clarify the real problem behind the request找出需求背后的真实问题
A request for emotion recognition may actually be a need for safety warning, training evaluation, service improvement, mental-health support, or role-fit assessment.一个“情绪识别”需求，背后可能真正需要的是安全预警、训练评价、服务改进、心理支持或岗位适配评价。
Build a small route before exposing a large system先做小路线，再做大系统
The lab can help partners test signal feasibility, task definition, model route, user interface, and evaluation metrics before committing to full deployment.实验室可帮助合作方先验证信号可行性、任务定义、模型路线、用户界面和评价指标，再进入完整部署。
Evaluate in the field在现场验证
Field validation exposes sensor noise, missing data, privacy boundaries, user trust, edge constraints, and maintenance cost.现场验证会暴露传感噪声、数据缺失、隐私边界、用户信任、边缘约束和维护成本。
Leave reusable capability behind留下可复用能力
A good case should leave methods, modules, patents, evaluation protocols, student talent, and a clearer route for the next product iteration.好的案例应沉淀方法、模块、专利、评测流程、学生人才和下一轮产品迭代路线。
The valuable work is often in the middle layer.真正有价值的能力，往往在中间层。
Human-state systems succeed when data governance, model orchestration, evaluation, field adaptation, and long-term operation work together. MAC-Lab's cases are designed to make that middle layer visible and discussable.人状态系统能否真正落地，取决于数据治理、模型编排、评测、现场适配和长期运行能否协同。MAC-Lab 的案例设计，就是把这层关键能力变得可看见、可讨论、可合作。
Best for partners who need a usable route.适合需要可用路线的伙伴。
MAC-Lab is a good fit when a partner has a real scenario, sensitive human-state questions, and a need for research credibility, engineering realism, and deployable solutions to mature together.当合作方有真实场景、敏感的人状态问题，并希望科学可信度、工程现实性和可落地方案共同成熟时，MAC-Lab 最适合参与。