Haven

Solving anxiety the stylish way. Haven is a fashion-tech garment that integrates ML-driven anxiety detection with real-time physical intervention, providing instant, discreet relief.

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TLDR:

  • 66% of college students report high anxiety—it’s common but hard to manage in busy lives. Things like meditation and weighted blankets aren’t always an option. We needed a solution that integrates seamlessly into our lives .
  • Haven is a garment that senses anxiety using our custom trained model, and provides instant relief through deep pressure and vibration therapy.
  • We're redefining how we manage stress—effortlessly, stylishly, and scientifically.

Inspiration

Anxiety is real. According to the American College Health Association, a staggering 66% of college students reported experiencing overwhelming anxiety in the past year. Even more concerning, 41.6% of college students meet the clinical criteria for an anxiety disorder. For us, we decided to combat this issue when we realized that there was no way to easily manage anxiety during those critical moments. As students, we juggle academic pressure, social expectations, and personal challenges—all of which can trigger anxiety. We've each had our share of panic attacks in public, moments where we felt isolated, unsure of how to stay calm. It's painful, and sometimes, help isn't within reach.

We asked ourselves: What if there was a way to make these moments easier? What if we could create something that not only helped us deal with anxiety in real time, but also fit seamlessly into our busy, social lives? A solution that wouldn't be obvious to others or require pulling out a gadget or device, but one that could work discreetly and automatically in the background.

Through research and conversation, we discovered that anxiety often showed up in physical signs: like a racing heartbeat, a hot flash, or maybe shallow breaths. These are things we could measure. Thanks to smart devices like the Apple Watch, we could track our biometric data—like heart rate variability, skin temperature, and motion patterns—and use this information to understand when anxiety was starting to take over. We also learned that there were a vast variety of therapies and technologies proven beneficial for anxiety, like deep pressure or vibration, that are currently underutilized.

Therefore, that became our goal: create a garment that could adapt to those triggers and respond with these underutilized therapies. That’s when we combined this issue with our passion for fashion tech: what if we created clothing that can help with anxiety?

This was the birth of Haven: the world’s first garment that detects and cares for anxiety.

What it does

Haven is a smart garment designed to detect and alleviate anxiety in real time. Using data from pre-your own wearables and biometric sensors embedded within the garment, it continuously monitors physiological indicators like heart rate variability, skin temperature, and breathing patterns. When it detects signs of anxiety—such as an elevated heart rate or erratic breathing—Haven automatically activates its built-in therapeutic mechanisms. These include deep pressure stimulation, mimicking the calming effect of a weighted blanket, and vibration therapy, which provides soothing rhythmic pulses to help regulate stress responses. All of this happens seamlessly, without the need for user intervention, allowing wearers to manage their anxiety discreetly and effortlessly.

How we built it

We used Python to develop and train machine learning models that analyze raw PPG data collected from long-term use of our wearable device. Our models leverage data from the Terra API, processing photoplethysmography (PPG) waveforms to classify heart rate patterns as “regular,” “irregular,” or “AFib.”

Beyond classification, our models generate personalized probability scores and severity indicators for various heart conditions, aligning with the diagnostic insights provided in our metadata.csv. By tracking heart rate variability (HRV) trends over time, our system can detect dysregulated patterns indicative of potential health risks. For example, elevated HRV in a chaotic pattern could suggest an underlying anxiety disorder, prompting users to seek further evaluation or medical intervention for long-term relief.

By combining wearable health monitoring, machine learning, and personalized cardiac risk assessment, our project empowers individuals with real-time insights into their heart health, supporting both preventative care and early diagnosis.

Tech Stack

Ideation & Research

  • Perplexity
  • ChatGPT
  • Claude
  • Cursor

Data Collection from Wearables

  • Apple Watch: Real-time data streaming via Swift in XCode
  • Terra API: Accessing sponsor-provided Garmin data
  • Firestore (Firebase): Storing real-time streaming data from wearables
  • MongoDB: Storing simulated Garmin data for testing
  • JavaScript (Node.js): Extracting and processing relevant data from MongoDB

Machine Learning & Pattern Recognition Machine Learning Frameworks:

  • Scikit-learn (traditional ML models)
  • TensorFlow/PyTorch (deep learning models)
  • PPG Signal Processing: HeartPy for feature extraction
  • Custom-Trained ML Model: Classifying PPG waveforms as Regular, Irregular, AFib

Data Handling: Pandas, NumPy for preprocessing and metadata fusion

Hardware & Actuation

  • Finite State Machine (FSM) for device control
  • Microcontroller: Arduino-based (C++)
  • Mechanical Design: Custom Fusion 360 mechanisms
  • Fabrication: 3D-printed components using Bambu Studio

Actuation & Stimulation: -Peltier cooling modules

  • Vibration motors
  • Linear actuator system (PWM powered)
  • Biosensors: Additional data backup to wearables
  • Assembly: Circuit soldering, garment pattern design, and sewing

UI/UX & Design

  • Figma: Prototyping & interface design
  • Adobe Illustrator: Visual assets & garment design

Challenges we ran into

Machine Learning Complexity We initially experimented with deep learning models, but simpler traditional ML models proved more efficient and accurate, leading us to pivot our approach mid-development.

Real-Time Wearable Data Extraction Extracting real-time data from the Apple Watch was challenging without an Apple Developer account. We built custom Swift scripts to pull HealthKit data, but transmitting it to our Arduino-connected clothing via Firestore caused compatibility issues and slow speeds. Ultimately, we simulated real-time streaming using TerraAPI’s Garmin data.

Hardware & Time Constraints Designing a functional wearable typically takes months—we had hours. We improvised with scrap materials, modified existing 3D models, and streamlined hardware-software integration to build our prototype within the hackathon timeframe.

Despite these hurdles, we adapted quickly and delivered a functional prototype for real-time cardiac monitoring and anxiety detection.

Accomplishments that we're proud of

Once the raw PPG data is correctly classified, our ML models generate probability scores and severity indicators with high accuracy!

Beyond functionality, we also focused on aesthetic design—our hardware device is not just wearable tech but a fashionable, trendy, and timeless top that seamlessly integrates health monitoring with style.

What we learned

  • Learn to be scrappy and use the existing resources around you to build a creative product (using studio scrap fabrics!!)
  • Don’t be afraid to ask, mentors and CAs helped so much in our project, especially as a beginner and starting out was very daunting but everyone was so willing to help

What's next for Haven

  • An extended line of clothing, the next in perhaps a more masculine flavor
  • Health warning signals: personalized insights on when anxiety attacks might be worth seen a doctor for
  • Perhaps a venture...?
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