Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud by Xinke Coco Wu

Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud by Xinke Coco Wu is a highly esteemed winner of the very prestigious 2026 A' Design Award Hlau in Tsim Thev Naus Laus Zis Xov Tooj, Apps thiab Software Competition Category.

HlauXov Tooj· 2026

Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud

by Xinke Coco Wu

Client: The Multiple Sclerosis Center of Georgia

A' Design Award Hlau Winner in Xov Tooj Category

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The Design

About Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud

Upgrove yog ib daim app hauv xov tooj ntawm tes rau kev ntsuas kev kho caj npab sab saud tom tsev. Daim app siv lub koob yees duab los txheeb kev txav ntawm cov xov tooj, ntsiav tooj thiab TV uas muaj koob yees duab sab hauv ntsej muag, yog li tsis tas yuav siv cov twj ntsuas hnav rau ntawm lub cev. Cov kev ntsuas kev txav raws kev kho mob tau raug hloov mus ua cov kev ua si me me uas muaj tsw yeeb yam ua liaj ua teb. Computer vision txheeb xyuas kev txav los kwv yees qhov ntev ntawm kev txav tau, qhov raug thiab qhov ruaj khov, thiab muaj ib tug qhab nia tshwm tuaj tom qab txhua txoj kev ua si. Cov ntawv qhia txog kev vam meej pab cov neeg mob taug qab lawv txoj kev rov zoo mus sij hawm ntev thiab pab cov kws kho mob saib xyuas lawv qhov kev ua tau deb ntawm chaw.

The Recognition

About the A' Design Award Hlau

Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud yog tus yeej ntawm A' Design Award Hlau, ib qho kev lees paub muaj meej mom uas muab rau cov qauv tsim zoo uas ua tau raws li cov kev xav tau ntawm kev ua haujlwm thiab kev lag luam uas xav tau los ntawm cov khoom tsim zoo. Cov neeg yeej Hlau qhia txog kev txawj loj, kev paub tshwj xeeb siab, kev nkag siab ntawm cov kws tshaj lij thiab lub peev xwm tsim ntawm cov neeg tsim. Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud xav tias yuav yog ib qho khoom tsim uas neeg hwm, qhuas thiab hlub uas siv tau thiab tsim tshiab. Cov qauv tsim yeej A' Design Award Hlau no sib xyaw cov kev coj ua zoo tshaj plaws ntawm kev lag luam thiab muaj cov yam ntxwv siv tshuab txawj ntse txuas nrog kev paub thiab txuj ci. Upgrove Daim Ntawv Thov Ntsuas Caj Npab Sab Saud tau txais kev hwm vim nws tag nrho thiab tsim los muab kev txhim kho rau lub neej uas pab ua rau lub ntiaj teb zoo dua.

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.