Upgrove 上肢评估应用程序 by Xinke Coco Wu is a highly esteemed winner of the very prestigious 2026 A' Design Award 铁奖 in 移动技术、应用与软件设计 Competition Category.

铁移动· 2026

Upgrove 上肢评估应用程序

by Xinke Coco Wu

Client: The Multiple Sclerosis Center of Georgia

A' Design Award 铁奖 Winner in 移动 Category

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Verified Award Winner

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The Design

About Upgrove 上肢评估应用程序

Upgrove 是一款用于居家上肢康复评估的移动应用程序。该应用在配备前置摄像头的普通手机、平板电脑和电视上采用基于摄像头的动作捕捉技术，无需佩戴任何可穿戴传感器。标准化的临床运动测试被转化为以农场为主题的小游戏。计算机视觉技术对动作进行分析，评估关节活动范围、准确度与稳定性，并在每次活动结束后生成评分。进度报告帮助患者追踪长期康复情况，同时便于临床医师远程查看其表现。

The Recognition

About the A' Design Award 铁奖

Upgrove 上肢评估应用程序是 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Upgrove 上肢评估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。Upgrove 上肢评估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。

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: UpGrove addresses a key gap in upper-limb rehabilitation, where accurate assessments are typically limited to clinical settings and recovery progress remains largely invisible between visits. The system translates standardized clinical motion tests into intuitive farm-themed activities that patients can perform at home. Camera-based motion capture analyzes mobility and coordination while gameplay scores objectively quantify performance. Continuous self-tracking, progress visualization, and rewarding feedback help patients stay motivated while enabling clinicians to monitor long-term recovery beyond the clinic.
Dimensions & Packaging: The system operates on standard cross-platform devices such as mobile phones, tablets, and TVs equipped with front-facing cameras. Computer vision models analyze skeletal movement patterns to estimate joint mobility and motion trajectories without requiring wearable sensors. The application provides real-time visual guidance, scoring mechanisms, and progress-tracking dashboards. Motion data are translated into measurable indicators such as range of motion, accuracy, and movement stability, enabling scalable and sensor-free rehabilitation assessment.
Tags: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
Production Technology: UpGrove is implemented as a mobile application using camera-based motion capture powered by computer vision algorithms. The system analyzes upper-limb movements in real time to estimate range of motion, stability, and control. Gesture recognition maps clinical motion patterns to interactive game mechanics, while data processing generates performance scores and actionable feedback. The design integrates motion tracking, gamification, and data visualization to support continuous rehabilitation assessment.
Design Challenge: The main challenge was translating clinically standardized upper-limb assessments into engaging digital interactions without compromising medical validity. Clinical tests are designed for accuracy rather than motivation, while game experiences prioritize engagement. The project required balancing these two perspectives by mapping therapeutic motions to intuitive gameplay mechanics. Another challenge was enabling reliable motion evaluation using device cameras without wearable sensors. Through close collaboration with clinicians and iterative prototyping, the design aligned clinical requirements with accessible interaction design.
Research: Mixed-methods design research was conducted to explore how clinical upper-limb assessments could become engaging gamified experiences. The study combined literature review, interviews with clinicians, domain experts, and patients, concept testing, usability testing, and technical validation. Data were gathered through clinical protocol analysis and motion interaction experiments using mobile cameras. Participants included 10+ clinicians and physical therapists and 3 patients. User testing results indicated that UpGrove significantly improved patients’ visibility of recovery progress, motivation to perform self-assessments, and accessibility of upper-limb rehabilitation beyond clinical environments.
Inspiration: UpGrove was inspired by the gap between clinic-based upper-limb assessments and patients’ everyday rehabilitation experiences at home. Many patients lack visibility into their long-term recovery progress and often feel discouraged during extended rehabilitation periods. In addition, traditional assessments typically require clinical visits, making frequent monitoring inconvenient for patients and clinicians alike. Research with clinicians, patients, and engineers inspired us to explore how clinical assessments could be translated into intuitive motion games and objective performance scores using device cameras. By transforming an often invisible and clinic-isolated recovery process into a visible and accessible experience, UpGrove aims to improve engagement, accessibility, and continuity in long-term rehabilitation.
Project Duration: The project was initiated in 2023 as part of human-computer interaction research on digital rehabilitation. Concept development, interaction design, and prototype implementation were completed between 2024 and 2025. The project was developed through interdisciplinary collaboration among designers, engineers, clinicians, and patients to explore accessible at-home rehabilitation assessment. The design is currently being implemented by the Georgia Tech Contextual Computing Lab in collaboration with the Georgia Multiple Sclerosis Center.
Operation Flow: Users launch UpGrove on a personal mobile device and create a personalized profile to receive tailored self-assessment tasks. On a regular schedule, patients perform guided upper-limb motion tests designed as farm-themed mini-games in their home environment. The front-facing camera captures body movements while the system analyzes gestures in real time. Visual cues guide users through therapeutic motions, and after each activity the system generates scores and progress feedback. Over time, the application builds longitudinal recovery reports that allow patients to understand their rehabilitation progress while also enabling clinicians to remotely review performance data and monitor patient recovery outside clinical visits.
Team Members: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
Image Credits: Images and visual materials were created by Xinke Wu (Designer), Jhih Rou (Iris) Sun (Designer) with AI-assisted generation tools like ChatGPT and Gemini, UpGrove Project, 2025.