Takeda – Twinnie X A-Wake 2025/26
An AI mirror tracks skin changes daily and prepares a one-pager for the doctor
Your health story, finally connected.

- Team
- 9 master students
- Universities
- Hasso Plattner InstituteUniversity of Science and Technology of ChinaHefei University of TechnologyAnhui University
- Disciplines
- Computer Science · Information Technology · Microelectronics · Digital Health · Industrial Design · Environmental Design
What happened, step by step.
Challenge
Digital health tools are fragmented, disease-focused and detached from daily life. Takeda asked for a next-generation interface that brings treatment, wellness and lifestyle together as chronic conditions rise.
What they built
Two connected parts. A-Wake, an AI-powered mirror, runs daily voice and image check-ins, detects changes in lesions, redness and scaling, and writes a doctor-ready one-pager before each appointment. Twinnie stores this in a patient-controlled profile with wearable data and documents.
Impact
Patients with chronic skin conditions would arrive better prepared and clinicians decide faster. For Takeda, the concept would provide real-world longitudinal data on disease progression and treatment response.
Full project text
The team’s original texts from the project page.
Twinnie x A-Wake is an AI-powered health ecosystem that turns daily skin monitoring into clinically meaningful conversations. Through a smart mirror and voice check-ins, it tracks symptom trends automatically and delivers a clear summary to both patient and doctor — making every appointment more informed, efficient, and human.
Challenge
Digital health tools have the potential to transform how people manage their wellbeing, yet current solutions remain fragmented, disease-focused, and disconnected from everyday life. As healthcare ecosystems grow more complex, there is a pressing need for interfaces that go beyond single-condition apps to support holistic, continuous health journeys. Takeda's challenge asks: how might we design a next-generation healthcare interface that seamlessly integrates treatment, wellness, and lifestyle? This is relevant as rising chronic conditions, health data abundance, and shifting user expectations demand smarter, more human-centered digital health experiences.
Solution
Twinnie x A-Wake is a two-part ecosystem bridging daily skin monitoring and clinical conversations. A-Wake uses an AI-powered mirror for daily voice and image check-ins, automatically detecting lesion changes, redness, and scaling trends over time. It generates a doctor-ready one-pager before each appointment. Twinnie stores this data in a patient-controlled health profile, unifying wearables, documents, and monitoring in one place. Together, they transform fragmented health data into structured, actionable insights — enabling patients to arrive better prepared and clinicians to make faster, more informed decisions.
Impact
Twinnie x A-Wake delivers measurable impact by transforming fragmented daily health data into structured clinical insights, reducing wasted appointment time and improving treatment decisions for patients with chronic skin conditions. By enabling patients to track over 90 days of symptom trends independently, the solution increases health autonomy and reduces anxiety between visits. For Takeda, it offers a strategic advantage by capturing real-world longitudinal data on disease progression and treatment response. These insights are invaluable for understanding patient journeys beyond clinical settings, informing drug development, and demonstrating treatment effectiveness. Ultimately, it bridges the gap between everyday health and clinical care.
Who built it.

Anupama Bishwokarma
Digital Health student

Ayse Alomar
M.Sc. Digital Health Student

Levani Khaduri
M. Sc. Computer Science Student

Jiangersen Bahejiang
B.Sc. Computer Science Student

Keju Wen
B.Sc. Industrial Design Student

Ming Fang
M.Sc. Design (Environmental Major) Student

Tingyu Zhu
B.Sc. Microelectronics Student

Wenrui Xu
M.Sc. Microelectronics Student

Zishun Liao
B.Sc. Information Technology Student