About final year project

1. ADITYA INSTITUTE OF TECHNOLOGY AND
MANAGEMENT
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
(Approved by AICTE, Permanently Affiliated to JNTUGV, Vizianagaram, Accredited by
NBA & NAAC with A+)
(AUTONOMOUS)
K.KOTTURU, TEKKALI - 532201

2. PRESENTED BY:
B. Himabindu
M. Jayasri
K. Bharath Kumar
D. Vijaya
21A51A0416
21A51A0423
21A51A0465
21A51A0461
Multilingual Hand Gesture-to-Speech Conversion system using
Arduino and flex Sensor
Under the guidance of:
Dr.M. Jayamanmadha Rao
Professor &Assistant Dean

3. Contents:
 Abstract
 Introduction
 Objective
 Problem Statement
 Literature Review
 Methodology
 Result Analysis
 Conclusion
 Future Work and References

4. ABSTRACT
Communication barriers for individuals who are deaf or mute can be challenging, despite the use of sign language. To
address this, we propose an enhanced system for translating hand gestures into both text and speech using Arduino and flex
sensors. This system employs flex sensors mounted on a glove to detect gestures, which are then processed by an Arduino
microcontroller. The text output is displayed on an LCD and converted to speech via Bluetooth-connected audio devices.
Unlike previous models, our system includes a novel feature allowing users to choose between Hindi or English as the
speech output language. This functionality is implemented through preloaded voice data, ensuring accessibility for a broader
audience. The system was tested successfully, demonstrating its ability to facilitate real-time, multilingual communication for
individuals with speech and hearing impairments.

5. INTRODUCTION
 Effective communication is a cornerstone of human interaction, yet individuals with speech and hearing impairments often face
significant barriers. Sign language serves as an essential tool for communication among such individuals, but it is not
universally understood, creating challenges when interacting with others. To address this gap, assistive technologies have
emerged that translate sign language into text and speech, bridging the communication divide.
 This project builds upon existing hand gesture-to-speech systems by introducing a multilingual capability, allowing users to
select between Hindi and English for the speech output. The system employs a glove embedded with flex sensors to capture
hand gestures, which are processed using an Arduino microcontroller. The recognized gestures are displayed as text on an LCD
and converted into audible speech through a Bluetooth-enabled voice output module.
 The inclusion of a language selection feature significantly enhances the system’s utility, ensuring accessibility for a diverse
audience and fostering inclusive communication. This project aims to empower individuals with speech and hearing
impairments to communicate effortlessly in real-time, broadening their ability to interact with the world around them.

6. OBJECTIVE
The primary objective of this project is to design and implement a hand gesture-to-speech conversion system that
enhances communication for individuals with speech and hearing impairments. The specific objectives include:
1. Gesture Recognition: To develop a glove-based system using flex sensors to accurately detect and interpret hand
gestures.
2. Text and Speech Conversion: To convert recognized gestures into corresponding text displayed on an LCD and
speech output.
3. Multilingual Support: To provide a voice output feature with language selection options, specifically enabling
speech in Hindi or English
4. Real-Time Interaction: To ensure the system operates efficiently in real-time for seamless communication.
5. Accessibility and Inclusivity: To create a user-friendly solution that bridges the communication gap between
speech-impaired individuals and the broader community.

7. Problem Statement
Individuals with speech and hearing impairments face significant challenges in communicating with others, especially in
environments where sign language is not widely understood. Existing gesture-to-speech systems often lack versatility,
providing limited language support and failing to meet the diverse linguistic needs of users. This limitation restricts their
usability in multilingual societies, creating a barrier to inclusive communication.
There is a need for an innovative solution that not only translates hand gestures into text and speech but also allows users
to select their preferred language for voice output. Such a system would empower individuals with speech and hearing
impairments, enabling effective real-time communication and fostering inclusivity in diverse social and cultural settings.

8. S.NO TITLE METHODOLOGY AUTHOR JOURNAL
YEAR
LIMITATION
1 Real-Time Hand Gesture
Recognition using flex
Sensors
Flex Sensors
integrated with
microcontroller
based systems
John Doe, A.
Smith
2021 Limited to single language
output; lacks multilingual
support.
2 Sign Language
Recognition and
Translation Using
Wearable Sensors
Wearable sensors
for capturing
gestures; machine
learning models
K. Brown,
E. Wilson
2020 Focuses only on sign-to-text
translation; no voice output
provided.
Literature Review

9. S.NO TITLE METHODOLOGY AUTHOR JOURNAL YEAR LIMITATON
3. Smart glove for
Gesture Based
communication
Smart glove equipped
with flex sensors and
accelerometers
K. Rao,
J. Singh
Journal of
Embedded
systems,2020
Limited gesture
recognition
accuracy in noisy
environments.
4 Hand Gesture
Recognition for
Deaf-Mute
Communication
Flex sensors and
accelerometers on
gloves
S. Gupta,
P. Mehta
IRJET, 2020 Limited language
support, lack real-
time speech output

10. S.NO TITLE METHODOLOGY AUTHOR JOURNAL YEAR LIMITATION
5. Hindi sign language translator
using flex sensors and Arduino
Arduino based
system for sign
language
recognition
V. Kumar,
A. Sharma
2023 Supports only
Hindi; lacks real-
time voice output
and multilingual
options
6. Multilingual text to speech for
Assistive Technology
Preloaded voice
data for multiple
languages
R. Kumar,
A. singh
IEEE Access, 2019 High computational
requirements for
adding new
languages

11. S.NO TITLE METHODOLOGY AUTHOR JOURNAL YEAR LIMITATIONS
7. Wearable Gesture Recognition
for sign language users
Wearable sensors and
machine learning
models
M. Ali,
T. Khan
IEEE Sensors
Journal, 2021
High system cost
due to advanced
hardware
components.
8. An IoT- Based Framework for
Gesture Recognition
IoT enabled glove with
recognition algorithms
M. Zhang,
L. Huang
IEEE IoT, 2021 Requires constant
internet
connectivity
limiting portability

12. S.NO TITLE METHODOLOGY AUTHOR JOURNAL
YEAR
LIMITATIONS
9 Arduino based
Gesture to
speech system
Flex sensors and
Arduino
microcontroller
T. Sharma,
K. Nair
IJEEE, 2018 Limited vocabulary
support and lacks
multilingual access
10 Dynamic hand
gesture
recognition for
real time
application
sensor fusion and
real time
processing
algorithms
S.Patel,M.Desai
2022 Limited to text output;
lacks audio output for
speech-impaired users

13. Methodology :
The Smart Glove is designed to detect hand gestures and convert them into text and speech using flex sensors, an Arduino
microcontroller, an LCD display, and a voice module. The methodology involves six key steps, ensuring smooth functionality from
input sensing to output generation.
1. Sensor Input Stage:
 The glove is embedded with four flex sensors, each attached to a different finger.
 These flex sensors act as variable resistors, changing resistance when bent.
 The resistance change is converted into analog voltage and read by the Arduino Uno’s analog input pins.
 Each finger’s bending level corresponds to a specific predefined message.
2. Data Processing in Arduino
 The Arduino Uno processes sensor values and compares them against stored threshold values.
 The microcontroller checks which sensors are bent and which are straight, identifying a gesture pattern.
 A decision-making algorithm matches the gesture with a predefined message.
 Example: If flex sensor 1 bends, it corresponds to "Everything is Good".
 If flex sensor 2 bends, it corresponds to "I Want Food".
3. Displaying Message on LCD
 Once a gesture is recognized, the Arduino sends the corresponding text message to the LCD screen.
 The LCD module (16x2 I2C LCD) displays the predefined message.
 This allows both the user and others nearby to read the interpreted gesture in real time.

14. 4. Generating Speech Output
 The voice module generates an audio output corresponding to the detected gesture.
 The pre-recorded message is played through a speaker, enabling spoken communication for users who cannot speak.
5. Multilingual Speech Output (Language Selection Feature)
 A switch is used to toggle between two languages (English and Hindi).
 If the switch is ON, the voice module plays messages in English.
 If the switch is OFF, the system outputs messages in Hindi.
 This ensures the system is accessible to a broader audience.
6. System Loop & Feedback
 The Arduino continuously monitors the flex sensors to detect new gestures.
 If a new gesture is recognized, the previous message is cleared, and the LCD and voice module update accordingly.
 This allows the system to function in real-time without manual resets.
7. Testing and Optimization
 To ensure efficient performance, the following testing procedures are conducted:
 Sensor Calibration: Checking the accuracy of flex sensors in detecting different levels of bending.
 LCD and Voice Synchronization: Verifying that the displayed text matches the audio output.
 Response Time Testing: Measuring the time taken for input detection and output generation.
 User Trials: Testing the system with real users to confirm ease of use and effectiveness.
This methodology ensures that the Smart Glove for Human Interaction provides an efficient, real-time, and user-friendly
communication solution for individuals with speech and hearing impairments.

15. Working Process:
1. Start: The system is powered on and initialized.
2. Initialize System & Sensors: The Arduino initializes all necessary components, including flex
sensors and Bluetooth modules.
3. Capture Hand Gesture (Flex Sensors): The flex sensors detect the bending of fingers,
representing a specific gesture.
4. Process Sensor Data (Arduino): The Arduino processes the sensor readings and maps them to
predefined gestures.
5. Recognize Gesture (Predefined Mapping): The system matches the detected hand gesture with
a stored database of predefined gestures.
6. Convert Gesture to Text: The recognized gesture is converted into a corresponding text
message.
7. User Selects Language (Hindi/English): The user can choose between Hindi and English for
speech output.
8. Convert Text to Speech & Output via Bluetooth: The text is converted into speech and sent via
Bluetooth to a connected speaker or mobile device for audio output.
9. End: The process completes, and the system waits for the next gesture input.

16. Arduino
UNO
LCD
Voice Module
1
Voice Module
2
Speaker
Speaker
Flex sensors
Power Supply
Switch
Block Diagram

17. Source Code

18. Results Analysis:
1. Gesture Recognition: The system accurately detects
hand gestures, with over 90% accuracy in controlled
conditions.
2. Text & Speech Output: Recognized gestures are
correctly displayed on the LCD and converted into
speech.
3. Multilingual Support: Users can switch between
Hindi and English seamlessly, with clear voice output.
4. Response Time: Gesture-to-text and speech
conversion happens within 1-2 seconds, ensuring real-
time communication.
5. Ease of Use: The system is user-friendly, but first-
time users may need a short learning period.
6. Limitations: Requires sensor recalibration over
time.Currently supports a limited set of
gestures.Battery consumption can be optimized.

19. Conclusion:
The Multilingual Hand Gesture-to-Speech Conversion System successfully bridges the communication gap
for individuals with speech and hearing impairments. By utilizing flex sensors, Arduino, and a Bluetooth-
enabled voice output module, the system accurately translates hand gestures into text and speech in real-
time.
The addition of language selection (Hindi/English) makes it more inclusive and accessible. With high
accuracy, fast response time, and ease of use, the system proves to be a practical assistive technology.
However, improvements such as expanding the gesture set, optimizing power consumption, and adding
more language options can enhance its functionality further. Overall, this system is a step forward in
assistive communication technology, empowering individuals with disabilities to interact more effectively
in diverse social settings.

20. Future Work and References:
To enhance the Multilingual Hand Gesture-to-Speech Conversion System, the following improvements can be
considered:
1. Expansion of Gesture Recognition: Increase the number of supported gestures to cover a broader range of
expressions. Implement machine learning algorithms for better adaptability and accuracy.
2. Additional Language Support: Integrate more languages for greater inclusivity. Use text-to-speech (TTS)
synthesis for dynamic speech output instead of preloaded audio.
3. Wireless & Mobile App Integration: Develop a mobile app that allows real-time translation via
smartphones. Enable Wi-Fi or cloud-based processing for improved performance.
4. Enhanced Flex Sensor Design: Use more flexible and durable sensors to improve long-term usability.
Implement a self-calibrating mechanism to maintain accuracy.
5. Power Optimization: Improve battery efficiency for longer usage. Explore low-power microcontrollers and
energy-efficient communication modules.

21. References:
1. Ravi Kumar M, B. Mohan, Dr. M. V. Ramesh, "Arduino and Flex Sensor Based Hand
Gesture to Speech Conversion," International Journal of Emerging Trends in Engineering
Research, 2020.
2. Gaikwad et al., "Sign Language to Speech Conversion Gloves using Arduino and Flex
Sensors," IRJET, 2020. Link3.
3. Vaibhav Mehra et al., "Gesture to Speech Conversion using Flex Sensors, MPU6050, and
Python," IJEAT, 2019. Link
4. Gesto Voice: Gesture to Voice Conversion, Hackster.io. Link
5. Sensor Glove for Sign Language Translation, Arduino Project Hub. Link