🖐️ Gesture Vocalizer – Bridging Communication Through Technology

📘 Overview

The Gesture Vocalizer project empowers individuals with speech and hearing impairments by translating hand gestures into speech or text output.
By combining flex sensors, accelerometers, and deep learning models, this wearable system bridges the communication gap — enabling real-time gesture-to-voice translation.

Fig 1. Glove

🧠 Proposed Design

The design phase focuses on translating the identified problem into a functional, wearable prototype that captures, processes, and vocalizes gestures.

Fig 2. Proposed Flow

🧩 Hardware Design

1. Arduino Mega Microcontroller

• Acts as the computational hub with multiple I/O pins.
• Handles real-time sensor integration and data transmission.

2. Flex Sensors

• Five sensors attached to a glove detect finger bending through resistance changes.
• Provide continuous analog data reflecting hand posture.

3. MPU-6050 Accelerometer

• Measures acceleration and angular velocity to capture spatial orientation.
• Enhances motion precision and dynamic gesture recognition.

⚙️ Hardware Design Workflow

1. Sensor Integration: Connect flex sensors to Arduino analog pins.
2. MPU-6050 Integration: Interface via I2C to capture acceleration and rotation.
3. Power Supply: Powered through USB to ensure stable operation.
4. Encapsulation: Mounted on a glove to maintain sensor stability and comfort.

💻 Software Design

1. Neural Network

• Implements a Bi-directional LSTM for gesture recognition.
• Processes sequences of sensor readings to identify dynamic and static gestures.
• Trained using a custom dataset for enhanced accuracy.

2. Custom Dataset

• Captures 3-second intervals of sensor data per gesture.
• Combines flex sensor and MPU-6050 outputs into a single input vector.
• Stored in CSV format for efficient model training and testing.

3. User Interface

• A simple Python-based UI displays recognized gestures in real time.
• Enables recording of new gesture data and live model inference.

Fig 3. User Interface

Fig 4. User Interface

Fig 5. User Interface

⚙️ Implementation

🔧 Algorithm Overview

Initialization

• Define analog pins for flex sensors (A0–A4).
• Initialize MPU6050 and set up I2C communication.

Setup Function

• Begin serial communication and initialize all sensors.
• Configure pin modes for flex sensors.

Loop Function

• Read analog flex sensor data.
• Capture accelerometer and gyroscope readings.
• Print all data to the serial monitor for real-time observation.

#include <Wire.h>
#include <MPU6050.h>

MPU6050 mpu;
int flexPins[5] = {A0, A1, A2, A3, A4};

void setup() {
  Serial.begin(9600);
  Wire.begin();
  mpu.initialize();
  for (int i = 0; i < 5; i++) pinMode(flexPins[i], INPUT);
}

void loop() {
  for (int i = 0; i < 5; i++) {
    int flexVal = analogRead(flexPins[i]);
    Serial.print(flexVal);
    Serial.print("\t");
  }
  int16_t ax, ay, az, gx, gy, gz;
  mpu.getMotion6(&ax, &ay, &az, &gx, &gy, &gz);
  Serial.println();
  delay(100);
}

🧾 Results and Readings

Model Performance Comparison Across Algorithms

🛠️ Tech Stack

⚡ Installation & Setup

1. Clone the Repository

git clone https://github.com/username/gesture-vocalizer.git
cd gesture-vocalizer

2. Install Python Dependencies

pip install -r requirements.txt

3. Upload Arduino Script

Use the Arduino IDE to upload the .ino file to your Arduino Mega board.

4. Run the Application

python app.py

5. Interact

Perform gestures wearing the glove — recognized gestures will appear in the UI and be vocalized.

📊 Performance Summary

🏆 Achievements

• Our research was published in the Journal of Electrical Systems, Vol. 20, No. 10s (2024).
• We obtained a copyright for our novel dataset.

🤝 Contributors

• Mahek Upadhye
• Aasmi Thadhani
• Urav Dalal
• Shreya Shah