Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ ብ Xinke Coco Wu

ዲዛይን Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ ብ Xinke Coco Wu ኣብ ናይ ውድድር ምድብ ንድፊ ሞባይል ቴክኖሎጂ፣ ኣፕሊኬሽንን ሶፍትዌርን ኣዝዩ ዝኸበረ ተሸላማይ ናይ 2026 ናይ ሓጺን ሽልማት A' Design Award እዩ።

ሓጺንሞባይል· 2026

Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ

ብ Xinke Coco Wu

ዓሚል: The Multiple Sclerosis Center of Georgia

ኣብ ምድብ ሞባይል ተሸላማይ ናይ ሓጺን ሽልማት A' Design Award

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ዝተረጋገጸ ተሸላማይ

ንቁጽሪ #182818 ሽልማት ኣረጋግጽ

ሳይት ብጻሕ ኣብ A' Design Award ርአዮ ጋዜጣዊ መግለጺ ባጅ ሽልማት

ሕብርታት

እቲ ዲዛይን

ብዛዕባ Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ

ኣፕግሮቭ ኣብ ገዛ ንዝግበር ናይ ላዕለዋይ ኣካላት (ኢድን ቅልጽምን) ተሃድሶ ገምጋም ዝዓለመ ናይ ሞባይል መተግበሪ እዩ። እዚ መተግበሪ ኣብ ልሙዳት ተሌፎናት፡ ታብሌታትን ናይ ቅድሚት ካሜራ ዘለወን ተለቪዥናትን ብካሜራ ዝሰርሕ ናይ ምንቅስቓስ መቐይሮ (ሞሽን ካፕቸር) ስለ ዝጥቀም፡ ዝኾነ ዝልበስ ሰንሰርታት ኣየድልዮን። ደረጃኦም ዝሓለዉ ክሊኒካዊ ናይ ምንቅስቓስ ፈተናታት ናብ ብሕርሻ ዝተሰነዩ ንኣሽቱ ጸወታታት ተቐይሮም ይቐርቡ። ኮምፕዩተራዊ ራእይ (ኮምፕዩተር ቪዥን) ንምንቅስቓስ ብምትንታን ስፍሓት ምንቅስቓስ፡ ልክዕነትን ርግኣትን ይግምት፡ ድሕሪ ነፍሲ ወከፍ ንጥፈት ድማ ነጥቢ ይወሃብ። ጸብጻባት ዕቤት ንሕሙማት ናይ ነዊሕ ግዜ ምሕዋዮም ክከታተሉ የኽእሎም፡ ንክሊኒካዊ ሰብ ሞያ ድማ ኣፈጻጽማ ብርሑቕ ክግምግሙ የኽእሎም።

እቲ ኣፍልጦ

ብዛዕባ ናይ ሓጺን ሽልማት A' Design Award

Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ ናይ ሓጺን ሽልማት A' Design Award ዝዓወተ እዩ፣ ካብ ጽቡቕ ዝተዳለዉ ፍጥረታት ዝጽበዩ ሞያውን ኢንዱስትሪኣውን ጠለባት ንዝመላኡ ጽቡቓት ዲዛይናት ዝወሃብ ክቡር ኣፍልጦ። ዘዓወቱ ሓጺን ዓቢ ክእለት፣ ልዑል ፍሉይነት፣ ክኢላዊ ርድኢትን ፈጠራዊ ዓቕምን ፈጣርዮም የርእዩ። Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ ብብዙሕ ዝተመነየ፣ ዝደነቐን ዝፈተዎን፣ ተግባራውን ሓድሽን ፍጥረት ክኾን ይጽበ። እዚ ዝዓወተ ዲዛይን ናይ ሓጺን ሽልማት A' Design Award ንዝበለጹ ልምድታት ኢንዱስትሪ የጣምር፣ ምስ ፍልጠትን ተውህቦን ዝተኣሳሰሩ ብቑዓት ቴክኒካውያን ባህርያት ድማ ኣለዎ። Upgrove ናይ ላዕለዋይ ኣካላት ገምጋም መተግበሪ ብጥንቃቐኡ ይኽበር፣ ንዓለም ዝሓሸ ቦታ ክትኸውን ዝሕግዙ ምምሕያሻት መዓርግ ህይወት ንምቕራብ ድማ ዝተዳለወ እዩ።

ድሕሪ እቲ ዲዛይን

ናይ ፕረስ ኪት

ክረዲትታት፣ ኣባላት ጋንታን ቴክኒካዊ ዝርዝራትን ብዲዛይነር ዝቐረቡ።

ፍሉያት ባህርያት: 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.
ዓቐንን ማዕሸግን: 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.
መለለዪታት: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
ናይ ምፍራይ ቴክኖሎጂ: 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.
ናይ ዲዛይን ብድሆ: 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.
ምርምር: 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.
ምንጪ ሓሳብ: 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.
ናይ ፕሮጀክት ግዜ: 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.
ናይ ስርሒት ፍሰት: 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.
ኣባላት ጋንታ: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
ናይ ስእሊ ክረዲት: 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.