Optimizing Real-Time Multimodal Data Integration and Machine Learning in a Mental Health App

Optimizing Real-Time Multimodal Data Integration and Machine Learning in a Mental Health App

I am currently working on a college project to build an app from scratch called WellnessWave, which provides real-time mental health interventions using machine learning. The goal of the app is to offer users personalized remedies based on three types of inputs:

• Speech
• Facial Expressions
• Text Inputs

These inputs are processed in real-time to offer suggestions like breathing exercises, meditation techniques, or light physical activities to help users improve their mental well-being. Below is a breakdown of my current implementation and challenges where I need help:

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1. Real-Time Data Collection:

• Tools Used:
  
  I’m using OpenCV for capturing facial expressions, external libraries for speech recognition, and text inputs collected through web forms.

• Directory Structure:
  
  I’ve created directories like speech_clips/, facial_expression_images/, and text_inputs/ to store the data.

Challenge:

I need help efficiently collecting and storing real-time multimodal data in a way that minimizes latency.

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2. Preprocessing:

• Tools Used:
  
  For data preprocessing, I’m using TensorFlow, MediaPipe, soundfile, and scikit-learn.

• Goal:
  
  Convert raw speech, image, and text data into meaningful features for model training and prediction.

Challenge:

I’m facing difficulties in synchronizing the preprocessing steps for multimodal data, especially with different types of input (speech, facial expressions, and text). What is the best way to synchronize and preprocess these data types?

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3. Multimodal Machine Learning:

• Approach: I’m building separate models for:
  • Speech Recognition
  • Facial Expression Analysis
  • Text Sentiment Analysis

These models will be combined using a multimodal fusion framework for real-time personalized mental health suggestions.

Challenge:

How can I efficiently integrate these models for real-time inference while ensuring low-latency and accurate multimodal fusion?

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4. Real-Time Processing:

• Current Setup: I have individual pipelines for speech, text, and image processing, but combining them in real-time is causing delays in recommendations.

Challenge:

I need help optimizing these pipelines to ensure seamless real-time integration of the data streams. What are some strategies to achieve low-latency processing with multimodal data?

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5. Adaptive Learning and User Feedback:

• Goal: Users will rate the suggestions provided by the app, and this feedback will be used to refine the model’s future recommendations.

Challenge:

How can I implement a feedback loop that allows the model to adapt and learn from user responses in real-time? What techniques or libraries can I use for online learning in Keras?

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Tools and Libraries:

• tensorflow
• opencv-python
• mediapipe
• soundfile
• scikit-learn
• joblib (for saving/loading models)

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Github and CodeSandbox Links:

• GitHub Repository
• CodeSandbox link included in the repository for web design.

I would greatly appreciate any suggestions on how to improve the data pipelines, real-time processing, or model deployment. Any insights on adaptive learning or multimodal fusion would also be incredibly helpful for this project. Thank you!

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