Strain Detecting Smart Chair

Abstract

Prolonged sitting with poor posture can lead to discomfort and health issues. This paper presents the design and implementation of a posture-detecting smart chair aimed at promoting ergonomic awareness and preventing discomfort associated with improper sitting positions. The proposed system utilizes an Arduino Uno microcontroller and ultrasonic sensors to monitor the user\'s posture in real-time. The system consists of an Arduino Uno microcontroller, an ultrasonic sensor (HC-SR04), power supply, connectivity. The ultrasonic sensor is strategically positioned to measure the distance between the chair and the user\'s back. The Arduino Uno processes the sensor data and notify the user to encourage a correct sitting posture when improper positioning is detected. The algorithm involves continuous monitoring of the ultrasonic sensor\'s readings. When the distance between the user and the chair falls below a predefined threshold, indicative of poor posture, the system notify user to change the posture. Additionally, feedback mechanisms such as LEDs or buzzers provide real-time alerts to the user, enhancing their awareness of their sitting habits. This low-cost, Arduino-based solution offers a practical and accessible means to address the increasingly prevalent issue of poor posture. The proposed smart chair contributes to the growing field of smart furniture, leveraging simple yet effective technology to promote health-conscious behaviors in daily life.

Introduction

I. INTRODUCTION

Welcome to the innovative realm of our Posture Corrector utilizing ultrasonic sensor technology and Arduino integration. In an era dominated by sedentary lifestyles, poor posture has become a prevalent issue with potential health implications. This project seeks to address this concern by introducing a cutting-edge solution that combines advanced sensing capabilities with a user-friendly interface.

This project introduces a Posture Detecting Smart Chair that combines ultrasonic sensor technology with Arduino to enhance ergonomic awareness and well-being. In an era dominated by sedentary activities, maintaining a proper posture is crucial for preventing health issues. The smart chair addresses this concern by strategically integrating ultrasonic sensors to monitor users' sitting positions. The system employs Arduino as a central processing unit, collecting and analyzing data from the ultrasonic sensors in real-time. By continuously assessing the distance and pressure distribution on the chair, the device provides valuable insights into the user's posture. In addition to detection, the chair offers interactive feedback mechanisms, gently nudging users toward better posture through subtle adjustments. This innovation is not only aimed at improving immediate comfort but also at cultivating long-term posture awareness. Beyond personal use, the Posture Detecting Smart Chair holds potential applications in various domains, such as office environments, healthcare facilities, and educational institutions. This technology aligns with the growing emphasis on proactive health measures in an increasingly digitized and sedentary world, offering a practical and accessible solution to promote healthier sitting habits and overall well-being.

This project not only focuses on immediate correction but also aims to in still lasting habits through continuous awareness. The implications of this technology extend beyond personal well-being, with potential applications in workplaces, healthcare settings, and educational environments. By merging convenience with health-conscious design, our Posture Corrector stands at the forefront of addressing the challenges posed by modern, sedentary lifestyles.

II. LITERATURE REVIEW

Posture is the position in which a person holds their body upright against gravity when standing, sitting or walking [5]. There are two approaches to determine posture, image-processing based and sensor-based. A sensor-based approach is implemented in two ways. One is by calculating the pressure distribution over di?erent weight distributing surfaces. The other is by calculating the angular di?erence between current posture and a pre-determined good posture.

The author states that the determination of sitting posture is mainly dependent on a chair and four sitting positions. Strength and ultrasonic sensors are installed inside the chair to acquire data. This is then processed using Principle Component Analysis to determine the condition of posture [6]. This approach solves the problems faced by a person when sitting and it is advantageous for users like students who are seated at a desk for a long period. But, it does not help a person correct their posture or practice good posture throughout. In another sensor-based approach to identify good sitting posture, two accelerometers are placed on di?erent parts of a person’s spine. With the help of two other sensors the goniometer and electrogoniometer the angle is calculated and posture is determined. This design is simple, e?ective and wearable but is only implemented on sitting positions [7]. An application speci?c approach to IoT based Smart Posture Detector 3 posture detection, where the device warns computer users when they lean too close to the computer, is the ”Postuino”. The device is not wearable but is instead placed next to the computer and when the distance between the computer, user and device falls below a speci?ed threshold, the device will alert the user. This is an innovative approach with a popular application and although it helps the user keep a safe distance from their electronic screen (i.e. computer), it does not help the user correct their actual back posture if the user is standing or walking [8-9].In another approach an actuator is used as a bio-mechanical posture detection device. This actuator shows sensory activity through an avatar in the application with which the device communicates. It assesses the user’s posture in the state in which the user is, i.e. sitting, standing, lying etc, to identify the user’s movement state or transition between movement states. While this might be a more advanced and applicable posture detector, it is also a more complicated and non-economical solution to the problem [10].Another technique uses inertial sensors for human posture detection in order to calculate three-dimensional angles of the human arm and hand. This information is recorded and later used to reconstruct it on a computer. This approach does not help rectify the back posture and does not give any feedback on how to correct it [11]. However, an intelligent chair can be adopted in order to classify and correct the posture of the user. Neural Networks are trained to classify the posture based on pre-trained standard postures. Though the approach to the problem is creative, it does not suggest correction for all postures of the user [12]. A novel solution is presented for wireless and wearable posture recognition based on a custom-designed wireless body area sensor network (WBASN), called WiMoCA. Here, sensors are represented by triaxial integrated MEMS (micro- electromechanical system) accelerometers. WiMoCA sensing node is designed to be wearable and low-power. It has a modular architecture to ease fast replacement and update of each component. The proposed method provides the complete implementation of a distributed posture recognition application [13-14]. The objective of an additional approach is to detect user’s postural changes, not to measure the pressure at each point precisely. The author Ricardo Barbaet al, states that the current mechanisms to detect postural changes are usually expensive, which greatly limits their use in e?ective computing. They have ruled out commercial solutions for two basic reasons: the need of adjusting the size of the sensors; the cost. To cope with the aforementioned challenges, their approach consists of combining several simple sensors so that they can be used together to form a posture sensor cushion [15].Another approach uses three main sensors - Accelerometer, Gyrometer and Bluetooth module. In this approach, the accelerometer measures the tilt of the 4 Greeshma Karanth, Niharika Pentapati, Shivangi Gupta body, the gyrometer measures the movement in the body and Bluetooth helps in connecting the belt with the phone app designed to display the readings[16]. The Arduino has been programmed for di?erent gestures and positions of the body that a person undergoes in everyday life. Although seeming e?cient, the belt measures the tilt of the lumbar region, when in fact, the actual tilt happens at the thoracic (i.e., the upper back) region of the spine. This could result in inaccuracy in posture correction [17]. One of the implementations suggests use of sensors (acceleration sensor) embedded in a smart phone contrary to the ones that require separate hardware components for the same. However, attaching the sensor to the phone and the phone to a belt is not a feasible solution as there is a risk of dropping the phone every time any rigorous activity is performed. Moreover, people use their phones extremely frequently, and for the most part of the day, it will be on their hands and not the belt [18]

III. PROBLEM STATEMENT

A problem statement serves to define the issues or challenges that a project or research aims to address. For a strain-detecting smart chair, the problem statement could be framed as follows:

A. Problem Statement

In contemporary society, prolonged sitting has become a prevalent aspect of daily life, contributing to a range of health concerns, including musculoskeletal disorders and discomfort. The conventional chairs lack the ability to provide real-time feedback on the strain experienced by individuals during prolonged sitting periods. Current solutions often focus on ergonomic design, but they do not incorporate dynamic strain detection to adapt to the changing needs of users.

This underscores the need for a novel approach—a strain-detecting smart chair—that can accurately monitor and analyze the strain experienced by users in real-time. Such a chair should integrate advanced sensing technologies capable of detecting subtle changes in posture, muscle tension, and pressure distribution. The challenge lies in developing a system that is not only sensitive and precise in strain detection but also practical, user-friendly, and cost-effective for widespread adoption.

The key issues to be addressed include:

1. Lack of Real-Time Strain Feedback: Current chairs do not provide users with immediate feedback on the strain experienced during prolonged sitting, hindering proactive adjustments to prevent discomfort and potential health issues.
2. Limited Integration of Advanced Sensing Technologies: Existing chairs often rely on basic ergonomic principles without incorporating cutting-edge sensing technologies. A strain-detecting smart chair requires the integration of advanced sensors capable of capturing nuanced biomechanical data.
3. User Adaptability and Customization: Individuals have diverse body types, habits, and preferences. Designing a smart chair that adapts to the unique strain patterns and postures of users, while allowing for customization based on individual needs, poses a significant challenge.
4. Practical Implementation and Affordability: Developing a smart chair with sophisticated sensing technologies must consider practicality and affordability. The challenge is to strike a balance between advanced functionality and widespread accessibility.
5. Data Security and Privacy Concerns: With the incorporation of sensors and data processing capabilities, ensuring the security and privacy of user data becomes paramount. Addressing concerns related to data collection, storage, and transmission is a critical aspect of developing a trustworthy smart chair.

In light of these challenges, the development of a strain-detecting smart chair emerges as a pressing need, promising to revolutionize the way individuals engage with their sitting environment and proactively manage the impact of prolonged sitting on their well-being.

IV. SOLUTIONS

Creating a posture detection smart chair using ultrasonic sensors involves combining sensor technology with intelligent algorithms for accurate and real-time monitoring. Here's a conceptual framework for the solution:

1. Ultrasonic Sensor Configuration

• Strategically place ultrasonic sensors on the chair, focusing on key points such as the backrest, seat, and armrests to cover a wide range of body positions.
• Optimize the sensor arrangement to minimize blind spots and ensure comprehensive coverage for accurate posture detection.

2. Real-Time Data Acquisition

• Configure the ultrasonic sensors to emit ultrasonic waves and measure the time it takes for the waves to reflect off the user's body parts.
• Implement a real-time data acquisition system to capture distance measurements from multiple sensors simultaneously.

3. Posture Recognition Algorithms

• Develop algorithms to analyze the ultrasonic sensor data and recognize different postures.
• Train machine learning models using labeled datasets to enable the chair to distinguish between various sitting positions, including correct and incorrect postures.

4. Calibration and Accuracy Enhancement

• Implement a calibration process to account for variations in individual body sizes and shapes.
• Fine-tune the algorithms to enhance accuracy by considering factors such as ambient conditions and potential interferences.

5. User Feedback Mechanism

• Integrate a feedback mechanism to provide real-time feedback to the user about their posture.
• Use visual or haptic cues to alert users when they adopt suboptimal postures and encourage corrective actions.

6. Posture Correction Features

• Implement adaptive features that automatically adjust the chair's ergonomics to encourage and maintain proper posture.
• Allow users to customize the degree of assistance or intervention based on their preferences.

7. User Profiles and Personalization

• Create user profiles to store individual preferences, historical posture data, and personalized feedback.
• Enable the chair to adapt its posture correction strategies based on the user's unique characteristics and habits.

8. Connectivity and Integration

• Enable the smart chair to connect to mobile or desktop applications for additional insights, analytics, and long-term tracking of posture habits.
• Integrate with smart home systems or wearable devices to create a comprehensive health monitoring ecosystem.

9. Energy Efficiency

• Design the system to operate efficiently to conserve power, ensuring that the chair can operate wirelessly for extended periods.
• Implement power-saving modes and sensors that activate only when needed.

10. User Education and Engagement

• Develop educational materials and interactive interfaces to help users understand the importance of good posture and the benefits of using the smart chair.
• Encourage user engagement through regular updates, challenges, and rewards for maintaining healthy sitting habits.

11. Usability Testing and Iterative Design

• Conduct usability testing with diverse user groups to gather feedback on the chair's effectiveness and user experience.
• Iterate on the design based on user input to continuously improve the chair's performance and features.

By combining these elements, a posture detection smart chair using ultrasonic sensors can offer a comprehensive solution for promoting healthy sitting habits and preventing musculoskeletal issues.

V. PROPOSED METHODOLOGY

Creating a strain-detecting smart chair using ultrasonic sensors and Arduino Uno involves a combination of hardware and software components. Here's a proposed methodology:

A. Hardware Setup

1. Ultrasonic Sensor Configuration

a. Place ultrasonic sensors strategically on the chair. For instance, position them on the seat and backrest.

b. Connect the ultrasonic sensors to the Arduino Uno. Ultrasonic sensors typically have a trigger pin, echo pin, VCC, and GND. Wire these according to the sensor's specifications.

2. Arduino Uno Connection

a. Connect the ultrasonic sensors to the Arduino Uno's digital pins for trigger and echo signals.

b. Connect the VCC and GND of the ultrasonic sensors to the appropriate pins on the Arduino Uno.

3. Power Supply

a. Power the Arduino Uno either through a USB connection or an external power source.

B. Software Development

1. Arduino Programming

   - Write an Arduino program to read data from the ultrasonic sensors.

   - Use the `NewPing` library or similar libraries that simplify ultrasonic sensor interfacing. Implement functions to calculate distance based on the time taken for the ultrasonic waves to bounce back.

   ```cpp

   #include <NewPing.h>

   #define TRIGGER_PIN  7

   #define ECHO_PIN     6

   #define MAX_DISTANCE 200

   NewPing sonar(TRIGGER_PIN, ECHO_PIN, MAX_DISTANCE);

   void setup() {

       Serial.begin(9600);

   }

   void loop() {

       int distance = sonar.ping_cm();

       Serial.println(distance);

       delay(1000);  // Delay for readability, adjust as needed

   }

   ```

2. Calibration

• Implement a calibration routine to account for variations in the ultrasonic sensor readings.
• Allow users to configure their baseline comfort level and tolerance for strain.

3. Strain Calculation Algorithm

• Develop algorithms to interpret the ultrasonic sensor data and calculate strain.
• Consider factors such as the duration of sitting, changes in posture, and user-specific parameters obtained during calibration.

By following this proposed methodology, you can create a strain-detecting smart chair using ultrasonic sensors and Arduino Uno, providing a foundation for promoting healthier sitting habits and preventing discomfort. Adjustments and refinements may be necessary based on specific user requirements and testing results.

VI. ACKNOWLEDGEMENT

I would like to express my profound gratitude to Dr. Nitin Janwe (H.O.D.), of CSE department, and Mrs. Madhavi Sadu (Mini Project Incharge) for their contributions to the completion of my project “STRAIN DETECTING SMART CHAIR".

I would like to express my special thanks to our mentor Mrs. Sharda Dabhekar for her time and efforts she provided throughout the semester. Your useful advice and suggestions were really helpful to us during the project’s completion. In this aspect, I am eternally grateful to you.

I would like to acknowledge that this project was completed entirely by us and not by someone else.

Your name

Shafiya Siddique

Vaishnavi Sriramula

Bhoomika Vishwakarma

Jyoti Yadav

Conclusion

In conclusion, a posture-detecting smart chair using ultrasonic sensors and Arduino presents a promising project with both immediate benefits and future potential. The current advantages include real-time posture feedback, customization options, and cost-effectiveness. However, challenges such as limited accuracy and maintenance requirements should be considered. Looking forward, the project\'s future scope involves integration with IoT, machine learning for personalized feedback, health monitoring features, and broader applications in various settings. As technology evolves, the chair could become an integral part of a holistic approach to health, incorporating biofeedback, gamification, and wearable integration. To maximize impact, ongoing research and development should focus on refining accuracy, enhancing user experience, and exploring partnerships with healthcare professionals. The posture detecting smart chair has the potential not only to improve individual well-being but also to contribute valuable data to the broader understanding of ergonomics and preventive healthcare measures. And, the future scope of posture detecting smart chairs using ultrasonic sensors and Arduino is promising. With advancements in technology and continued research, these smart chairs have the potential to revolutionize the way we approach posture correction and overall health. Integration with other smart devices, enhanced accuracy, accessibility, personalized health insights, remote monitoring, gamification, sustainability, and user experience design are all areas that can be further developed to maximize the potential of these smart chairs. By addressing these aspects, smart chairs can become an indispensable tool for promoting better posture, preventing musculoskeletal issues, and improving overall well-being for individuals across various settings.

References

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Copyright

Copyright © 2023 Bhoomika Vishwakarma, Jyoti Yadav, Shafiya Siddique, Vaishnavi Sriramula. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.