Upgrove مٿين عضون جي جائزي واري ايپليڪيشن by Xinke Coco Wu

Upgrove مٿين عضون جي جائزي واري ايپليڪيشن by Xinke Coco Wu is a highly esteemed winner of the very prestigious 2026 A' Design Award لوهه انعام in موبائيل ٽيڪنالاجيز، ايپليڪيشنز ۽ سافٽويئر ڊيزائن Competition Category.

لوههموبائيل· 2026

Upgrove مٿين عضون جي جائزي واري ايپليڪيشن

by Xinke Coco Wu

Client: The Multiple Sclerosis Center of Georgia

A' Design Award لوهه انعام Winner in موبائيل Category

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The Design

About Upgrove مٿين عضون جي جائزي واري ايپليڪيشن

اپ گروو هڪ موبائيل ايپليڪيشن آهي جيڪا گهر ۾ مٿين عضون جي بحاليءَ جي جائزي لاءِ ٺاهي وئي آهي. هيءَ ايپليڪيشن عام فونن، ٽيبليٽن ۽ اڳئين ڪئميرا وارن ٽيليويزنن تي ڪئميرا تي ٻڌل حرڪت جي گرفت (موشن ڪيپچر) استعمال ڪري ٿي، تنهنڪري ڪنهن به پائڻ جوڳي سينسر جي ضرورت نه ٿي پوي. معياري ڪلينيڪل حرڪت جي ٽيسٽن کي زرعي موضوع وارين ننڍڙين راندين (مني گيمز) ۾ تبديل ڪيو ويو آهي. ڪمپيوٽر وزن حرڪت جو تجزيو ڪري حرڪت جي حد، درستگي ۽ استحڪام جو اندازو لڳائي ٿو، ۽ هر سرگرمي کان پوءِ هڪ اسڪور ڏنو وڃي ٿو. ترقيءَ جون رپورٽون مريضن کي پنهنجي ڊگهي مدي واري صحتيابي تي نظر رکڻ ۽ ڊاڪٽرن کي پري کان ڪارڪردگيءَ جو جائزو وٺڻ جي سهولت ڏين ٿيون.

The Recognition

About the A' Design Award لوهه انعام

Upgrove مٿين عضون جي جائزي واري ايپليڪيشن A' Design Award لوهه انعام جو کٽيندڙ آهي، معزز اعتراف جيڪو انهن سٺين ڊيزائنن کي ڏنو وڃي ٿو جيڪي چڱيءَ طرح ڊزائين ڪيل تخليقن کان متوقع پيشه ورانه ۽ صنعتي گهرجون پوريون ڪن ٿيون. لوهه جا کٽيندڙ پنهنجي خالقن جي وڏي مهارت، اعليٰ ماهري، ماهرانه سمجھ ۽ تخليقي صلاحيت ڏيکارين ٿا. Upgrove مٿين عضون جي جائزي واري ايپليڪيشن کان اميد ڪئي وڃي ٿي ته اهو هڪ تمام گهڻو قدر ڪيل، ساراهيل ۽ پياري تخليق هوندو جيڪو عملي ۽ جدت وارو آهي. هي ڊيزائن جنهن A' Design Award لوهه انعام کٽيو آهي، صنعت جا بهترين طريقا گڏ ڪري ٿو ۽ علم ۽ صلاحيت سان ڳنڍيل قابل ٽيڪنيڪل خاصيتون رکي ٿو. Upgrove مٿين عضون جي جائزي واري ايپليڪيشن جي سنجيدگيءَ ڪري عزت ڪئي وڃي ٿي ۽ اهو زندگيءَ جي معيار ۾ بهتري آڻڻ لاءِ ٺاهيل آهي جيڪا دنيا کي بهتر جڳهه بڻائڻ ۾ مدد ڪري ٿي.

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.