Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè láti ọwọ́ Xinke Coco Wu

Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè láti ọwọ́ Xinke Coco Wu jẹ́ aṣẹ́gun olókìkí púpọ̀ ti 2026 Àmì-ẹ̀yẹ Irin A' Design Award nínú Ẹ̀ka Ìdíje Àpẹẹrẹ Ìmọ̀-ẹ̀rọ Alágbèéká, Ohun-èlò Fóònù àti Sọ́fítíwéà.

IrinAlágbèéká· 2026

Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè

látọ̀wọ́ Xinke Coco Wu

Oníbàárà: The Multiple Sclerosis Center of Georgia

Aṣẹ́gun Àmì-ẹ̀yẹ Irin A' Design Award nínú Ẹ̀ka Alágbèéká

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Aṣẹ́gun Àmì Ẹ̀yẹ Tí a Jẹ́rìísí

Jẹ́rìísí Ipò Àmì Ẹ̀yẹ fún #182818

Ṣàbẹ̀wò Ojú Òpó Wo ní A' Design Award Ìwé Ìròyìn Àmì Ẹ̀yẹ

Àwọ̀

Àpẹrẹ Náà

Nípa Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè

Upgrove jẹ́ ohun elo alágbèéká fún ìgbéléwọ̀n ìtọ́jú ìmúpadàbọ̀sípò apá òkè ní ilé. Ohun elo náà ń lo ìmúṣẹ̀mí ìgbékalẹ̀ ìṣíwọ́ pẹ̀lú kámẹ́rà lórí àwọn fóònù, tábúlẹ́ẹ̀tì àti tẹlifíṣàn tí ó ní kámẹ́rà iwájú, nítorí náà kò sí ìnílò fún àwọn sẹ́nsọ̀ tí a fi wọ̀ ara. Àwọn ìdánwò ìṣíwọ́ ìwòsàn tí a ti ṣètò déédé ni a yí padà sí àwọn eré kékeré tí ó ní àwòrán oko. Ìran kọ̀mpútà ń ṣàyẹ̀wò ìṣíwọ́ láti wọn ìwọ̀n ìṣíwọ́, ìpéye àti ìdúróṣinṣin, a sì ń mú àmì àyọrísí jáde lẹ́yìn iṣẹ́ kọ̀ọ̀kan. Àwọn ìròyìn ìtẹ̀síwájú ń jẹ́ kí àwọn aláìsàn lè tẹ̀lé ìmúláradá wọn fún ìgbà pípẹ́, ó sì ń jẹ́ kí àwọn oníṣègùn lè ṣàyẹ̀wò ìṣesí wọn láti ọ̀nà jíjìn.

Ìdánimọ̀ Náà

Nípa Àmì-ẹ̀yẹ Irin A' Design Award

Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè jẹ́ olùborí Àmì-ẹ̀yẹ Irin A' Design Award, ìdánimọ̀ olókìkí tí a fún àwọn àwòṣe tó dára tí wọ́n pàdé àwọn ohun tí iṣẹ́ àti ilé-iṣẹ́ nílò tí a retí láti ọ̀dọ̀ àwọn ẹ̀dá tí a ṣàwòṣe dáradára. Àwọn olùborí Irin ń fi ìjáfáfá ńlá, ìmọ̀ pàtó gíga, òye ògbóǹtarìgì àti agbára ìṣẹ̀dá àwọn olùṣẹ̀dá wọn hàn. A retí pé Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè yóò jẹ́ ẹ̀dá tí a kà sí gidigidi, tí a gbóríyìn fún tí a sì nífẹ̀ẹ́, tó wúlò tó sì ní ìmúṣẹ̀dá tuntun. Àwòṣe tó borí Àmì-ẹ̀yẹ Irin A' Design Award yìí ń kó àwọn ọ̀nà tó dára jùlọ ilé-iṣẹ́ jọ, ó sì ní àwọn ànímọ́ ìmọ̀-ẹ̀rọ tó péye tí a so mọ́ ìmọ̀ ṣíṣe àti ẹ̀bùn. A bọ̀wọ̀ fún Upgrove Ohun Elo Ìgbéléwọ̀n Apá Òkè nítorí ìfọ̀kànbalẹ̀ rẹ̀, a sì ṣàwòṣe rẹ̀ láti pèsè ìmúdára ìgbésí ayé tó ń ran ayé lọ́wọ́ láti di ibi tó dára jù.

Lẹ́yìn Àpẹrẹ Náà

Àkójọ Ìròyìn

Àwọn ẹ̀tọ́, àwọn ọmọ ẹgbẹ́ àti àlàyé ìmọ̀ ẹ̀rọ tí olùṣàpẹrẹ pèsè.

Àwọn Ohun Àkànṣe: 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.
Ìwọ̀n àti Àpò: 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.
Àwọn Àkọlé: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
Ìmọ̀-ẹ̀rọ Ìṣelọ́pọ̀: 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.
Ìpèníjà Àpèsè: 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.
Ìwádìí: 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.
Ìmísí: 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.
Àkókò Iṣẹ́ Àkànṣe: 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.
Ìlànà Ìṣiṣẹ́: 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.
Àwọn Ọmọ Ẹgbẹ́: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
Ìfọwọ́sí Àwòrán: 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.