UX/UI, PRODUCT DESIGN // 2026
LUCID
Lucid is a biometric mental health monitoring app built to bridge the gap between therapy sessions. Connected to a smartwatch, it passively collects biometric data, detects stress and mood spikes, and alerts users when meaningful changes occur. By combining these signals with user feedback, Lucid helps users recognize and understand their emotional patterns over time.
It strengthens continuity of care by turning this data into therapist-ready insights and personalized interventions informed by the user’s therapeutic journey. Instead of leaving users unsupported between sessions, Lucid creates an ongoing layer of care that helps both patient and therapist work from a clearer, more complete picture.
// Was developed as part of the Design Thinking course at Tel Aviv University, mentored by Tamar Many in a cross-disciplinary team of designers, engineers, and MBA students, and won 'Best Project'.
We worked closely with Dr. Craig Katz, a world-renowned psychiatrist from Mount Sinai in New York, who validated the process throughout and guided our clinical framing, research direction, and the language we implemented throughout the product.
[DETAILS]
TYPE
UX/UI Product Design
YEAR
2026
PLATFORM
Mobile
TOOLS
Figma, Base44
[THE PROBLEM]
Therapy happens once a week, life does not.
Between sessions, there are 167 hours of lived experience that usually go uncaptured: stress spikes, emotional dips, triggering moments, and subtle patterns that shape a person’s mental state. By the time therapy begins, much of that week has already been flattened by memory, filtered by hindsight, or forgotten entirely.
As a result, patients struggle to make sense of what they felt and when it happened, and therapists enter each session with only a partial picture, relying on whatever their patient can recall in the moment.
[THE SOLUTION]
167 hours of missing data, found.
Lucid sits passively in the background - using your smartwatch and phone to monitor biometric signals in real time, it collects physiological data throughout the week, detects stress and mood spikes, and alerts users when meaningful changes occur.
The app combines biometric signals with user feedback to surface patterns over time, generate therapist-ready insights, and offer personalized support shaped by the user’s therapeutic journey. In doing so, Lucid turns the undocumented hours between sessions into a clearer, more continuous picture of care.
[HOW MIGHT WE]
HMW make what happens between therapy sessions visible and understandable to both patients and their therapists?
[MAIN FLOWS]
[DETECTION & TREATMENT]
[DAILY SUMMERY]
[WEEKLY REPORT & INSIGHTS]
[PERSONA]
Yoav Ben Ami
Age: 34
Occupation: Project Manager
Yoav has been in therapy for eight months,
but when asked about his week, he often
blanks. Between stress, noise, and blurred
memories, he struggles to bring the right
things into the room and leaves feeling like
he only touched the surface.
Dr. Rachel Cohen
Age: 47
Occupation: Therapist
Rachel sees 25 patients a week, but still
enters each session with only a partial view
. She relies on what patients remember to
share, knowing that much of their week
gets lost between experience
and reflection.
[THE PROCESS]
Reseach and ideation
To shape the product direction, we spoke with both patients coping with post-traumatic stress and therapists who support them. These conversations gave us a clearer understanding of the emotional and clinical gaps that exist between sessions, and helped ground the project in real needs rather than assumptions. In parallel, we ran internal brainstorming sessions and took part in two focused workshops with mentors from Wix. There, we refined the user story, challenged our assumptions, and explored how the product could realistically function in practice. This process helped us move from an abstract opportunity space to a concrete concept we could sketch, test, and develop further.
Clinical Validation in Mount Sinai, NY with De. Craig Katz
Throughout the project, we worked closely with Dr. Craig Katz, Senior Psychiatrist at Mount Sinai Hospital in New York. He reviewed our research, challenged our assumptions, and helped ensure the product was grounded in a clinically responsible framework. He also validated the language used throughout the app, an especially critical part of designing in the mental health space, where small wording choices can significantly affect trust, interpretation, and emotional safety. Dr. Katz also directed us toward emerging research on facial recognition as an emotional indicator, which went on to shape one of Lucid’s core stress-detection features.
Early Exploration
Before defining the final visual language, we used Base 44 and Figma Make to quickly prototype Lucid’s core flows, calibration, home, and daily review.
This helped us test the experience early without spending time on polish.
Seeing it in motion made it clear what worked, what felt intuitive, and what needed to be rethought before moving forward.
User Feedback
We interviewed both patients and therapists, and their feedback helped us understand the most effective direction for the product. Patients responded strongly to the idea of an app that could help them feel supported between sessions, while therapists saw clear value in receiving biometric data and early signals before the appointment. These conversations helped us validate the need, sharpen the product direction, and better define how Lucid should continue evolving.
Outcome
Lucid translated an overlooked gap in mental health care into a clear and compelling product concept. By focusing on the undocumented hours between therapy sessions, we designed a system that supports patients with greater self-awareness and gives therapists a fuller, more actionable view of their patients’ week. The project was awarded in Tel Aviv University’s Design Thinking course.
[MAIN SCREENS]
[DETECTION]
[REPORTING & TREATING]
[FACE DETECTION]
[WEEKLY SUMMARY]
© 2026 MAYA SEGAL