Yixuan Song and Jiahui Yu 的作品 Soulnest 移动应用程序

设计师 Yixuan Song and Jiahui Yu 的作品 Soulnest 移动应用程序荣获了极具声望的 2026 A' Design Award 铁奖，竞赛类别为移动技术、应用与软件设计。

铁移动· 2026

Soulnest 移动应用程序

设计师： Yixuan Song and Jiahui Yu

客户: SoulNest

移动类别A' Design Award 铁奖获奖者

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验证 #182422 的获奖状态

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关于 Soulnest 移动应用程序

Soulnest 是一款为支持心理健康而打造的移动应用程序。该应用提供人工智能陪伴助手，引导用户完成四个步骤：陪伴角色选择、状态评估、心理疗愈与危机纾解。通过面部分析与心率感应，陪伴助手能够实时调整其语气与引导方式。应用运用认知行为疗法，帮助用户管理情绪压力。所有数据处理均在设备端完成，因此该应用可离线使用，并确保个人数据的私密性。

荣誉认可

关于A' Design Award 铁奖

Soulnest 移动应用程序是 A' Design Award 铁奖得主，这是授予满足人们对设计精良的创作所期待的专业与工业要求的优良设计的声望认可。铁奖得主展现出其创作者的高超技艺、高度专业化、专家级的理解力和创造能力。预计Soulnest 移动应用程序将成为一件备受推崇、令人钦佩且被珍爱的创作，既实用又创新。这件赢得 A' Design Award 铁奖的设计整合了行业最佳实践，并拥有与专业知识和才华相结合的称职技术特性。Soulnest 移动应用程序因其严谨而受到尊重，旨在提供有助于让世界变得更美好的生活品质改善。

设计幕后

媒体资料包

由设计师提供的致谢、团队成员及技术详情。

独特属性: Soulnest pioneers a paradigm shift in mental health by replacing traditional high-barrier appointments with an immediate, 4-step stabilization loop. It democratizes emotional care through de-stigmatized Ai avatars, delivering personalized support in under 10 seconds. Engineered for digital equity, it features on-device Ai inference for full offline functionality on low-end devices. Adhering to Wcag standards and low-arousal aesthetics, Soulnest fosters global resilience with proven 72% efficacy.
尺寸与包装: Ai architecture: Green Llm framework optimized via Tensorflow lite for local edge-computing. Performance: On-device inference latency less than 120ms; 40% reduction in peak Cpu load via adaptive computation dimming. Bio-data: Heart rate sampling at 30fps; 100% on-device physiological processing (zero-cloud privacy). Universal equity: Lightweight build less than 150mb for low-end device compatibility. Compliance: Full Wcag 2.1 level Aa and Iso-standard data encryption for global digital wellness.
标签: Mental Wellness, Ai Companion, Social Impact, Emotional Intelligence, Inclusive Design, Biofeedback, Digital Health, Wearable Interaction, Cbt Therapy, Accessibility
生产技术: Soulnest implements an empathic interaction system by merging Rppg bio-sensing with Facs-based facial tracking. This transforms passive interfaces into a responsive biofeedback loop, where Ai avatars dynamically adapt their tone and guidance based on real-time physiological stress markers. The design utilizes atomic design systems and flutter for cross-platform fluidity, ensuring a seamless experience that balances clinical rigor with a low-arousal visual language.
设计挑战: The hardest part was achieving emotional authenticity while ensuring absolute privacy. Internal factors: We had to balance clinical rigor with a low-arousal Ux to prevent digital fatigue. External factors: Overcoming the limitations of on-device Ai inference was crucial to allow full functionality on low-end hardware without cloud dependency. Result: We successfully engineered a responsive bio-feedback loop that senses crisis through subtle physiological cues, navigating complex data-protection laws while delivering life-saving immediacy.
研究: We conducted a multi-modal mixed-methods study to bridge the 75% gap in immediate mental support. Methodology: Using evidence-based design and longitudinal tracking, we integrated Cbt frameworks with Ai interaction. Tools and participants: 1,200 diverse users were monitored via Rppg sensors and biometric wearables over 6 months. Results: Data showed a 72% increase in emotional regulation and a 58% reduction in crisis duration. Impact: The research proves that Ai-driven, bio-responsive tools can democratize mental healthcare, transforming high-barrier therapy into a scalable, stigma-free social infrastructure.
灵感: Inspired by the "silent crisis" where 75% of those in need lack mental support due to stigma and cost, Soulnest was born to bridge this gap. We observed that the "waiting period" is the deadliest barrier. Our goal was to create a "seamless emotional infrastructure" that is as natural as breathing. By integrating evidence-based therapy with Ai-driven empathy, Soulnest replaces medical coldness with human-centric warmth, ensuring support is a right, not a luxury, at the very moment of crisis.
项目周期: The project started in Nov 2024 in San Jose and launched the beta version in Aug 2025.
操作流程: 1. Selection: Users choose a personalized Ai companion based on visual aesthetics and emotional affinity. 2. Diagnosis: The Ai assesses mental states by analyzing facial micro-expressions and conversational patterns. 3. Therapy: Users access anytime Ai consultations with avatar using Cbt techniques for immediate emotional support. 4. Crisis relief: Interactive scenarios and sensory healing are triggered to de-escalate emotional spikes. 5. Biometric tracking: Wearable sensors monitor physiological indicators, allowing the Ai to proactively intervene and track long-term wellness trends.
团队成员: Yixuan Song
Jiahui Yu
Xianxi Liao