CalmAlert

Breathe easy, we’ve got you.

Overview

CalmAlert is a desktop application, currently built with Python’s Tkinter library, designed to manage and monitor stress levels through simulated data. It calculates a stress score based on heart rate (HR) and environmental noise levels (decibels, dB), providing recommendations for managing stress. Eventually, we aim to scale CalmAlert into a smartwatch application.

Features

Stress Score Calculation: Uses heart rate and environmental noise data to compute a real-time stress score.
Data Simulation: Generates heart rate and decibel data through custom functions and Python’s random module for testing.
Recommendations: Provides gentle recommendations to help users manage stress based on calculated scores.
Built with Tkinter: Provides a simple and interactive desktop interface.

Technical Details

Stress Calculation Functions

CalmAlert calculates stress based on two primary metrics: heart rate and environmental noise.

Heart Rate (HR) Stress Level Calculation

   import math

   def hr_stress_level(user_avg_hr, user_cur_hr, k=0.02):
       hr_diff = user_cur_hr - user_avg_hr
       hr_stress = 50 * (1 - math.exp(-k * hr_diff))
       return hr_stress

This function computes stress based on the difference between the user’s current and average heart rates, applying an exponential scaling factor.

Decibel (dB) Stress Level Calculation

   def db_stress_level(decibel_value, threshold=70, k=0.07):
       if decibel_value > threshold:
           scaled_stress = 50 * (1 - math.exp(-k * (decibel_value - threshold)))
           return min(scaled_stress, 50)
       else:
           return 0

This function calculates stress from environmental noise levels, capping the stress score at 50.

Overall Stress Level Calculation

   def overall_stress_level(user_average_hr, user_current_hr, db_value, threshold=70):
       hr_stress = hr_stress_level(user_average_hr, user_current_hr)
       db_stress = db_stress_level(db_value)
       stress_level = hr_stress + db_stress
       return stress_level

Data Simulation Functions

To mimic real-world variations in heart rate and noise, we use the following simulation functions:

Normal Decibel Variation (targets below 70 dB)
Stressed Decibel Variation (targets avove 70 dB)

Example implementations:

import random

# Normal decibel variation
def normal_decibel_variation(cur_value, target=60, max_change=3):
    if cur_value < target:
        change = random.uniform(0, max_change)
    elif cur_value > target:
        change = -random.uniform(0, max_change)
    else:
        change = random.uniform(-max_change, max_change)

    next_db = cur_value + change
    return max(40, min(next_db, 80))

# Stressed decibel variation
def stressed_decibel_variation(cur_value, target=90, max_change=4):
    if cur_value < target:
        change = random.uniform(0, max_change)
    elif cur_value > target:
        change = -random.uniform(0, max_change)
    else:
        change = random.uniform(-max_change, max_change)

    next_db = cur_value + change
    return max(70, min(next_db, 100))

Running the Application

Clone the repository.
Install the necessary dependencies.
Run the main application file using: bash python main.py

Future Goals

Our goal is to transform CalmAlert into a fully functional wearable application for smartwatches, where it will continuously monitor and alert users of high-stress situations in real time.

Authors

Sanaa Bhaidani - Backend Developer
Jacob Mellick - Full-Stack Developer
John Alvaro - Full-Stack Developer
Grace Padberg - Backend Developer

What's next for CalmAlert

In addition to transforming CalmAlert into a smartwatch application, we envision several future features and integrations to enhance the user experience:

Breathing Routine Trigger: In cases where CalmAlert detects a consistently high heart rate, it could automatically trigger the Apple Watch Breathing routine to help the user manage stress in real-time.
Expanded Health Tracking:
- Heart Rate Variability (HRV): Monitoring HRV can provide deeper insights into stress levels and overall cardiovascular health.
- Menstrual Cycle Tracking: Including cycle tracking data may allow for more personalized stress management insights, as stress and heart rate can fluctuate based on hormonal cycles.
- Sleep Tracking: Integrating sleep data will help users understand how stress and sleep patterns interact, encouraging better sleep habits for stress reduction.
- Physical Activity Tracking: This feature could help differentiate between exercise-induced heart rate increases and stress-related spikes.
Workout Mode & App Integration:
- To avoid misinterpreting elevated heart rates during physical activities, the app would allow users to select a workout mode. This lets the app know the user is actively exercising rather than in a stressful situation.
- Manual Overrides: Users could disable the app temporarily in specific contexts (e.g., during intense workouts or when in known noisy environments). After a certain period, the app may send a reminder to turn it back on if it's been disabled for too long.

These extensions would enhance CalmAlert’s adaptability and make it more user-friendly across various life situations, ensuring it provides accurate stress assessments and valuable, personalized insights.