Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz ītēchcopa Xinke Coco Wu

Tlanāhuatīlli Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz ītēchcopa Xinke Coco Wu cē cencā mahuizzō tēmacēhualōni īpan huēyi 2026 Mahuizolli A' Design Award Tepoztli īpan Oliniatepoztli, Tlahtolchihualli ihuan Tepoztlahtolamatl Tlachihchihualiztli Nēhuilīztli Tlamantli.

TepoztliOliniatepoztli· 2026

Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz

īpal Xinke Coco Wu

Tlanāhuatīlli: The Multiple Sclerosis Center of Georgia

Mahuizolli A' Design Award Tepoztli Tēmacēhualōni īpan Oliniatepoztli Tlamantli

7,279

Tlaneltīlīlli Tēmacēhualōni

Xictlaneltilia Tēmacēhualli ītēchcopa #182818

Xiquitta Tlahcuilōlcalli Xiquitta A' Design Award ītechcopa Āmatl Tēmachiyōtīliztli Tlamachiōtl

Tlapalli

Tlanēxtilistli

Ītechcopa Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz

Upgrove ce aplicación mobile tlen mochihua ipan calli inic motta quenin mopahtia in acopa maitl (upper limb). Inin aplicación quitequitia in cámara ic quiquixtia in olinilizmachiyotl ipan teléfonos, tabletas ihuan televisiones tlen quipiya cámara ixpan, ic amo monequi in sensores tlen mopiloa ipan tonacayo. In tlaneltililiztli clínica olinilizmachtiliztli tlen ye tlatlalilli mocuepa quen mini-juegos ica millan-tlamantli ixiptla. In visión computacional quiixtemoa in olinilizli inic quixtemoa quexqui hueyi in olinilizli (range of motion), in melahualiztli ihuan in chicahualiztli, ihuan ce tlapohualiztli (score) moquixtia zatepan cecen tequitl. In amoxtli tlen quinextia in nenemilizli quincahuilia in cocoxqueh ma quittacan quenin mopahtiah huehcauh, ihuan quincahuilia in tepahtianime ma quittacan in tequipanoliztli huehca.

Tēixīmachōtiliztli

Ītechcopa Mahuizolli A' Design Award Tepoztli

Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz ca in quicuic in Mahuizolli A' Design Award Tepoztli, tlamahuizoliztli tlen momaca in cualli dizayno in quitlalia in tlen monequi ipan tequitl ihuan tlachihualiztli in quimaca in cualli tlachihualli. In quicuicah Tepoztli quinextia in hueyi tlamatiliztli, toltecayotl ihuan ihuelitiliztli intlachihuanime. Ca quinequi in Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz ca tlazohtli, mahuiztic, yancuic ihuan motequiuhtia. Ni dizayno in quicuic Tepoztli quihuelitia in tlamatiliztli ihuan quipia chicahualiztli tlen tepoztli. Upgrove Tlatlaliliztli ic Motta in Acopa Maitl Ipatiliz quicualtia in nemiliztli ihuan quihuelitia in cemanahuac ica cualli.

Tlanēxtilistli Ītēixpan

Āmatl Tlanextīliztli

Tlapōhualli, tēquitqueh īwan tēchmachilīztli ōquinmaca in tlanēxtīlīlli.

Cē Zan Ītēchcopa: 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.
Huēyiliztli īhuān Tlacualtepēhualli: 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.
Tōcāitl: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
Tlachīhualiztli Māchiyōtl: 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.
Tlanāhuatīlli Nētēcahuilīztli: 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.
Tlatēmōliztli: 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.
Tēyōllālīliztli: 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.
Tēquitl Cāhuitl: 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.
Tēquitl Ōhuitl: 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.
Calpolli Tlācah: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
Īxiptla Tlanēxtīlli: 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.