HackStack

Project Info

MindShift: AI for Bipolar Care

Devpost

Inspiration

With the rise of smartwatches and an increasingly tech-savvy generation, physiological data tracking has gained significant attention. However, its application for bipolar patients—who could benefit the most—remains largely unexplored. MindShift aims to fill this critical gap by leveraging biometric data to enhance monitoring, early intervention, and personalized support.

What it does

MindShift bridges the gap between bipolar patients and their therapists or support networks. Using biometric data from the Apple Watch, we monitor key physiological indicators like heart rate variability, sleep patterns, activity levels, and agitation—metrics that research has linked to extreme mood episodes. When significant shifts occur, MindShift detects them and initiates an adaptive conversation with the patient. Simultaneously, this data is synced to the Therapist Dashboard, providing real-time access to patient biometrics and behavioral assessments. The dashboard also generates AI-driven recommendations to guide patients back to stability and help therapists make informed decisions. By restoring social connection and emotional balance, MindShift strengthens the support system for those who need it most.

How we built it

MindShift consists of two core components: the watch experience and the therapist interface. The watch experience utilizes the Terra API to collect real-time biometric data. Conversations with patients are facilitated using ElevenLabs and Gemini/Mistral AI for adaptive dialogue. Data is stored in ChromaDB and visualized in the React.JS therapist dashboard, which provides real-time alerts, detailed assessments, and AI-driven intervention suggestions in an intuitive interface.

Challenges we ran into

We faced two major challenges: Integrating Terra API – Mapping real-time biometric data to meaningful mental health insights required custom data labeling and extensive testing. Deploying to a physical Apple Watch – Due to short-term Apple data policy restrictions, we had to simulate the full experience digitally rather than running it on a live device.

Accomplishments we're proud of

We take immense pride in designing and implementing a fully connected backend and database infrastructure—a significant architectural challenge given the complexity of real-time data processing. Despite the constraints of a hackathon, we successfully built a robust system that combines biometric tracking, AI-powered dialogue, and therapist-facing insights.

What we learned

Our team gained valuable experience in deploying edge AI solutions and navigating the challenges of integrating real-time physiological and behavioral data. Additionally, through extensive research on bipolar disorder, we deepened our understanding of how digital tools can complement traditional mental health care.

What's next

for MindShift Moving forward, we aim to collaborate with bipolar patients and therapists to refine our system based on real-world needs. We’re also exploring additional behavioral and physiological indicators that could further enhance detection and intervention—potentially contributing to a deeper understanding of bipolar disorder as a whole.

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • FlaskIn code
  • HTMLIn code
  • JavaScriptIn code
  • PythonIn code
  • ReactIn code
  • SwiftIn code
  • Tailwind CSSIn code
  • Google GeminiClaimed
  • Mistral AIClaimed

8 of 10 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

146 KB

Source files

35

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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