Upgrove Augšējo ekstremitāšu novērtēšanas lietotne — Xinke Coco Wu

Darbs Upgrove Augšējo ekstremitāšu novērtēšanas lietotne, kura autors ir Xinke Coco Wu, ir augsti novērtēts un ļoti prestižās 2026 Dzelzs balva A' Design Award ieguvējs kategorijā Mobilo tehnoloģiju, lietotņu un programmatūras dizains.

DzelzsMobilais· 2026

Upgrove Augšējo ekstremitāšu novērtēšanas lietotne

Autors: Xinke Coco Wu

Klients: The Multiple Sclerosis Center of Georgia

Dzelzs balva A' Design Award ieguvējs kategorijā Mobilais

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Dizains

Par Upgrove Augšējo ekstremitāšu novērtēšanas lietotne

Upgrove ir mobilā lietotne augšējo ekstremitāšu rehabilitācijas novērtēšanai mājās. Lietotne izmanto uz kameru balstītu kustību uztveršanu standarta tālruņos, planšetdatoros un televizoros ar priekšējām kamerām, tāpēc valkājami sensori nav nepieciešami. Standartizēti klīniskie kustību testi ir pārveidoti par lauku saimniecības tēmas minispēlēm. Datorredze analizē kustības, lai novērtētu kustību amplitūdu, precizitāti un stabilitāti, un pēc katras aktivitātes tiek aprēķināts rezultāts. Progresa pārskati ļauj pacientiem sekot līdzi ilgtermiņa atveseļošanai un ļauj klīnicistiem attālināti pārskatīt sniegumu.

Atzinība

Par Dzelzs balva A' Design Award

Upgrove Augšējo ekstremitāšu novērtēšanas lietotne ir Dzelzs balvas A' Design Award ieguvējs, prestiža atzinība, ko piešķir labiem dizainiem, kas atbilst profesionālajām un rūpnieciskajām prasībām, kādas sagaidāmas no labi izstrādātiem darbiem. Dzelzs ieguvēji parāda to radītāju lielo prasmi, augsto specializāciju, ekspertu izpratni un radošo spēju. Sagaidāms, ka Upgrove Augšējo ekstremitāšu novērtēšanas lietotne būs augsti vērtēts, apbrīnots un cienīts darbs, praktisks un inovatīvs. Šis ar Dzelzs balvu apbalvotais dizains integrē nozares labāko praksi un tam ir kompetentas tehniskās īpašības, kas saistītas ar zinātību un talantu. Upgrove Augšējo ekstremitāšu novērtēšanas lietotne tiek cienīts par rūpību un ir izstrādāts, lai uzlabotu dzīves kvalitāti un palīdzētu padarīt pasauli labāku.

Aiz dizaina

Preses materiāli

Dizainera sniegtie autori, komandas dalībnieki un tehniskie dati.

Unikālās īpašības: 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.
Izmēri un iepakojums: 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.
Birkas: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
Ražošanas tehnoloģija: 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.
Dizaina izaicinājums: 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.
Pētījums: 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.
Iedvesma: 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.
Projekta ilgums: 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.
Darbības plūsma: 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.
Komandas dalībnieki: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
Attēlu autori: 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.