Revolutionizing Communication: My Smart Gloves Project for the Hearing and Speech Impaired

Revolutionizing Communication: My Smart Gloves Project for the Hearing and Speech Impaired

Introduction

Communication is a fundamental human need, yet millions face challenges due to hearing and speech impairments. Inspired to bridge this gap, my team and I developed Smart Gloves—a wearable device that translates hand gestures into speech or text. Here’s a behind-the-scenes look at our journey, from conception to realization, and the tech hurdles we overcame.

Project Overview

Objective: Our mission was clear: create a wearable device to aid communication for deaf and mute individuals by translating hand gestures into speech or text.

Technologies Used:

• Hardware: Arduino Mega 2560, Flex Sensors, MPU6050 (Accelerometer and Gyroscope)

• Software: Arduino IDE, Python, Random Forest Algorithm for machine learning

Team:

• Aditi Shenoy

• Akshay Prakash

• Atif Kundlik (My Role)

• Pranav Menon

My Role:

• Assembled and interfaced hardware components with the Arduino Mega 2560.

• Collected and preprocessed data for the machine learning model.

• Implemented and trained the Random Forest Classifier for gesture recognition.

• Integrated the system to translate gestures into speech or text outputs.

Development Journey

We started with extensive research and planning, followed by designing and prototyping. Our gloves incorporated flex sensors to capture finger movements and an MPU6050 sensor to track hand orientation. Using Arduino IDE, we programmed the gloves to recognize specific gestures. Data collection was crucial; we recorded numerous gestures, creating a robust dataset for our machine learning model.

Overcoming Challenges

Sensor Accuracy: Ensuring accurate readings in different environments was tricky. We fine-tuned our sensors and used data smoothing techniques.

Data Collection: Creating a comprehensive dataset was labor-intensive. We meticulously recorded and labeled gestures to train our model effectively.

Gesture Distinction: Differentiating similar gestures to avoid misinterpretation was challenging. We fine-tuned our algorithm to enhance accuracy.

Real-Time Processing: Ensuring the system processed and output gestures in real-time required optimizing our code for efficiency.

Results and Outcomes

Our Smart Gloves prototype successfully translated hand gestures into speech and text with impressive accuracy. The positive feedback highlighted its potential to improve the quality of life for the hearing and speech impaired.

Technical Insights

Arduino Code: Our Arduino code reads sensor values and transmits them for processing.

cppCopy codevoid loop() {
  // Reading sensor values
  sensorVal[1] = analogRead(sensor2);
  // More sensor readings...
  printData();
}

void printData() {
  Serial.print(String(sensorVal[1]));
  // Printing more data...
}

Python Code: The Python script processes the data using a Random Forest Classifier.

pythonCopy codeimport pandas as pd
from sklearn.ensemble import RandomForestClassifier

# Data preprocessing
df = pd.read_csv('gestures.csv')
X = df.drop('sign', axis=1)
y = df['sign']

# Model training
model = RandomForestClassifier()
model.fit(X, y)

Future Prospects

Our project has tremendous potential for future enhancements, such as incorporating more gestures, improving accuracy with advanced algorithms, and integrating with mobile apps for wider accessibility.

Conclusion

Our Smart Gloves project embodies innovation and compassion, aiming to make the world more inclusive for the hearing and speech impaired. Explore our journey and the detailed technical implementation on our https://github.com/AtifInCloud/Smart-Gloves-Project.git

I'm also proud to share that we published a paper on this project, highlighting the technical aspects and impact of our innovation.
https://ijsrem.com/volume-04-issue-10-october-2020/