Upgrove Asnas n Uktazal n Yiɣil Ufella sɣur Xinke Coco Wu

Upgrove Asnas n Uktazal n Yiɣil Ufella sɣur Xinke Coco Wu d anerbaz yettwaqadren meqqren n 2026 Tarbaḥt A' Design Award n Uzzal deg Taggayt n Tnaflit n Afeṣṣel n Tetiknulujiyin Tizirawin, Isnasen d Useɣẓan.

UzzalAziraw· 2026

Upgrove Asnas n Uktazal n Yiɣil Ufella

sɣur Xinke Coco Wu

Amsaɣ: The Multiple Sclerosis Center of Georgia

Anerbaz n Tarbaḥt A' Design Award n Uzzal deg Taggayt n Aziraw

7,279

Anerbaz n Urraz Yettwasenqden

Senqed Addad n Urraz i #182818

Rzu ɣer usmel Wali deg A' Design Award Tabḍert n tɣamsa Tanecḍa n wammud

Initen

Afeṣṣel

Ɣef Upgrove Asnas n Uktazal n Yiɣil Ufella

Upgrove d asnas azirig i uktazal n uɛiwed n tezmert n yiɣallen ufella deg uxxam. Asnas-a yesseqdac takamiṛat i uṭṭaf n umussu ɣef yitilifunen imagnuten, tafliwin tumḍinin akked tiliẓriwin yesɛan takamiṛat n zdat, ihi ur ilaq ara ad tettwals kra n lḥess (sensors) yettwalsen. Ikayaden isnajyanen izeɣzanen n umussu ttwabeddlen ɣer turaren timeẓyanin yesɛan asentel n tfellaḥt. Tamuɣli n uselkim tesleḍ amussu akken ad tesmuqel azal n telɣa n umussu, tiseddi akked urkad, yerna yal armud yettak-d amḍan (score). Ineqqisen n usnerni ttaǧǧan imuḍan ad ḍefren aḥlu-nsen di lweqt aɣezfan, u ttaǧǧan imejjayen ad muqlen tigawt-nsen si lebɛid.

Asmekti

Ɣef Tarbaḥt A' Design Award n Uzzal

Upgrove Asnas n Uktazal n Yiɣil Ufella d amerbaḥ n Tarbaḥt A' Design Award n Uzzal, tagmat i yisnulfa yelhan i yemmalen ayen i d-ttrajun yimdanen seg yisnulfa yelhan. Imerbaḥen n Uzzal ttbanen d tmusni, tmahilt d tazmert n yimahilen-nsen. Upgrove Asnas n Uktazal n Yiɣil Ufella ad yili d asnulfu yettwaɛezzen, yelhan, amaynut. Isnulfa-a ttwaɛezzen imi ɛawnen deg tudert, rran ddunit ugar n lxir.

Deffir n ufeṣṣel

Tagrurt n tɣamsa

Ismawen n terbaɛt, iferdisen itiknikanen akked telɣut i d-yefka umeṣṣufeɣ.

Taɣara Tasuft: 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.
Tigejdiwin & Akemmus: 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.
Tibzimin: Digital Health, Upper-Limb Recovery, Sensor-Free Motion Capture, Computer Vision, Gamified Therapy, Gamified Assessment, Gesture Interaction, Motion Games
Tasnakta n Ufares: 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.
Asewḥel n Usnulfu: 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.
Anadi: 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.
Aseḥbiber: 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.
Tanzagt n Usenfar: 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.
Tanila n Umahil: 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.
Igmamen n Terbaɛt: Design Lead: Xinke (Coco) Wu
Visual Designer: Jhih Rou (Iris) Sun
Motion Designer: Yameng Li
Imsuren n Tugniwin: 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.