Design and Development of Head Motion Controlled Wheelchair using IOT Devices

Design and Development of Head Motion Controlled Wheelchair using IOT Devices

International Journal of Engineering Research & Technology

ISSN: 2278-0181

Vol. 14 Issue 04, April-2025

Siddhesh Pakhare, Prof. Pralhad Narwade, Neeraj Sherkhane, Manasi Kamble MGMs College of Engineering and Technology, Navi Mumbai Department of Electronics and Telecommunication Engineering

BE students, Assistant Professor

Abstract – Floods The Head Motion Controlled Wheelchair represents a significant advancement in assistive technology, designed to enhance mobility and independence for individuals with quadriplegia due to spinal cord injuries, strokes, or cerebral palsy. Traditional power wheelchairs often rely on joystick controllers, which may be unsuitable for users with limited hand mobility. To address this challenge, the proposed system employs a MEMS sensor (MPU6050) to detect head tilting movements in four directions: forward, backward, right, and left. These motion signals are processed by a microcontroller, enabling precise wheelchair navigation. Unlike existing hands-free control systems that are often costly and intrusive, this design offers an affordable, non-intrusive alternative. The integration of smart technology also facilitates obstacle detection and improved maneuverability, enhancing user safety and convenience. This paper explores the systems working model, components, and potential impact on improving the quality of life for individuals with mobility impairments.

Keywords- Head motion control, Smart wheelchair, Assistive technology, Quadriplegia, MEMS, IoT, Obstacle detection, Hands-free mobility.

1. INTRODUCTION

   The Head Motion Controlled Wheelchair is an innovative advancement in assistive technology, designed to improve mobility and independence for individuals with quadriplegia caused by conditions such as spinal cord injuries, strokes, or cerebral palsy [1]. Many quadriplegic individuals struggle to operate traditional powered wheelchairs due to the lack of hand and arm control. To address this challenge, the proposed system incorporates a MEMS sensor (MPU6050) that detects head tilting movements in four directions forward, backward, right, and left. These signals are processed by a microcontroller, which translates the movements into precise wheelchair navigation commands. Unlike existing hands-free control methods, which can be bulky and costly, this design provides an affordable and non-intrusive alternative [2]. By enabling hands-free movement, real-time navigation, and obstacle detection, this smart wheelchair enhances user autonomy and accessibility. This paper explores the systems design, functionality, and its potential to improve the quality of life for individuals with severe mobility impairments.

   1. Existing System

      Conventional electric wheelchairs primarily use joystick controllers for navigation, providing users with an efficient

      means of movement. However, these systems require hand and arm mobility, making them unsuitable for individuals with severe physical impairments. To overcome this limitation, alternative control methods such as sip-and-puff systems, voice commands, and brain-computer interfaces have been explored. While these technologies enhance accessibility, they often come with drawbacks like high costs, complexity, and limited adaptability. Thus, there is a need for a more affordable, intuitive, and user-friendly mobility solution, leading to the development of head motion-controlled wheelchairs.

      1. Conventional Wheelchair- A conventional manual wheelchair is a commonly used mobility aid that requires users to propel it using hand rims or depend on a caregiver for movement. While it is affordable, lightweight, and easy to maintain, it has significant limitations, particularly for individuals with severe mobility impairments such as quadriplegia. Quadriplegic individuals, who experience partial or complete paralysis in all four limbs due to spinal cord injuries, strokes, or neuromuscular disorders, lack the upper body strength and control needed to operate a manual wheelchair. This makes them entirely dependent on caregivers, reducing their independence and freedom of movement. Additionally, navigating long distances, inclines, or uneven terrain manually can be exhausting and impractical, leading to fatigue, joint strain, and discomfort over time. Unlike powered wheelchairs, manual models do not offer smart features like obstacle detection, automatic braking, or remote control operation, limiting safety and usability. Moreover, prolonged use of manual wheelchairs can result in pressure sores and posture-related issues, further affecting the user's well-being. Given these challenges, manual wheelchairs are not a viable option for quadriplegic patients, emphasizing the need for advanced, hands-free mobility solutions such as head motion- controlled wheelchairs that enhance accessibility, comfort, and independence [3].

      2. Joystick Controlled Wheelchair- Conventional electric wheelchairs are primarily operated using joystick controllers, which allow users to navigate by manually adjusting the joystick in different directions. These wheelchairs provide a reliable and widely adopted mobility solution for individuals with physical disabilities.In addition to joystick control, some advanced models incorporate touchpad controls or switch-based mechanisms, which offer

         alternative input methods for users with limited dexterity. Some systems also feature proportional control, where the speed and direction of the wheelchair are adjusted based on the force applied to the joystick. While these systems are effective for many users, they pose challenges for individuals with severe motor impairments, such as quadriplegia or neuromuscular disorders, who may lack the hand strength or coordination required to operate them. This limitation has led to the development of hands-free control methods, including voice commands, sip-and-puff systems, and brain-computer interfaces, to improve accessibility for users with limited mobility [4].

      3. Drawback And Limitations of Existing System-

      The existing wheelchair systems, including joystick- controlled, sip-and-puff, voice-controlled, and brain- computer interface (BCI) models, have several drawbacks and limitations. Joystick-controlled wheelchairs require hand and arm mobility, making them unsuitable for quadriplegic users. Sip-and-puff systems and voice- controlled models can be challenging to use in noisy environments and may not respond accurately to users with respiratory or speech impairments. BCI-based wheelchairs, while innovative, are highly complex, expensive, and require extensive calibration. Additionally, many existing systems lack real-time obstacle detection, automated braking, and IoT-based remote monitoring, limiting user safety and accessibility. These limitations highlight the need for a cost-effective, hands-free, and intuitive mobility solution to improve the independence and quality of life for individuals with severe physical impairments.

   2. PROPOSED SYSTEM

      The prevailing challenge faced by individuals with quadriplegia, stemming from conditions like spinal cord injuries, strokes, or cerebral palsy, lies in their limited ability to control conventional power wheelchairs due to the absence of hand movement control. Existing hands-free controller systems, while aiming to provide solutions, often prove obtrusive and financially burdensome. The pressing need for an innovative, cost-effective, nd non-intrusive mobility solution has inspired the development of the Head Motion Controlled Wheelchair. This project addresses the critical problem of restricted independence and mobility among quadriplegics by harnessing the power of MEMS sensors to interpret head movements for precise wheelchair navigation [5]. The project aims to overcome the current limitations in assistive technology, offering an effective and user-friendly alternative that enables quadriplegics to perform essential tasks, navigate freely, and enhance their overall quality of life.

      1. System Overview

         The Individuals with quadriplegia, resulting from conditions such as spinal cord injuries, strokes, or cerebral palsy, face significant challenges in controlling conventional power wheelchairs due to their inability to use hand-operated controllers [6]. While hands-free wheelchair control systems exist, they are often expensive, bulky, and not easily accessible. To address this issue, this project proposes the Head Motion Controlled Wheelchair, an affordable, non- intrusive, and user-friendly mobility solution. The system

         Vol. 14 Issue 04, April-2025

         integrates MEMS sensors (MPU6050) to detect head movements, allowing users to navigate the wheelchair by tilting their heads in four directions: forward, backward, right, and left. These signals are processed by a microcontroller, which translates the detected movements into precise navigation commands. The system also incorporates IoT-based monitoring, obstacle detection, and emergency alert features to enhance safety and usability. By providing a cost-effective and intuitive alternative to existing solutions, this project aims to restore independence to quadriplegic individuals, enabling them to perform daily tasks, move freely, and improve their overall quality of life.

         Key features of the proposed system:

         The proposed Head Motion Controlled Wheelchair offers a cost-effective, non-intrusive, and user-friendly mobility solution for individuals with quadriplegia and severe motor impairments. It utilizes a MEMS sensor (MPU6050) to detect head movements in four directions, which are processed by an Arduino Uno microcontroller for precise navigation. The system includes obstacle detection using ultrasonic sensors, IoT integration for real-time monitoring and emergency alerts, and an emergency stop mechanism for enhanced safety. Additionally, it features adjustable sensitivity to accommodate different user needs and operates on a rechargeable battery, ensuring efficient and continuous mobility. By eliminating the need for hand control, this wheelchair significantly improves accessibility, independence, and quality of life for users [7].

         Head Motion Control The wheelchair is controlled through head movements detected by an MPU6050 MEMS sensor. Users can tilt their heads forward, backward, left, or right to navigate in the respective direction. This feature eliminates the need for hand or arm movement, making it highly suitable for individuals with quadriplegia or severe motor impairments.

         Arduino Uno Processing The system is powered by an Arduino Uno microcontroller, which processes signals from the MEMS sensor and translates them into movement commands. These commands control the motor driver module, ensuring smooth and precise navigation of the wheelchair. The microcontroller provides real-time response, reducing lag and improving user experience.

         IoT Integration The system incorporates Internet of Things (IoT) technology, enabling real-time monitoring and remote access. Through an IoT-enabled interface, caregivers or family members can track the wheelchairs location, receive emergency alerts, and monitor battery status. This feature enhances security and accessibility, providing peace of mind for both users and caregivers [8].

      2. Transmitter Module

         The transmitter module is responsible for collecting real- time environmental data and transmitting it to the receiver module for monitoring. This module includes:

         MEMS Sensor (MPU6050) – The MEMS sensor detects head movements using an integrated accelerometer and gyroscope. It captures tilt and angular velocity when the

         user moves their head in different directions, such as forward, backward, left, or right. The detected motion signals are then sent to the Arduino for processing.

         Arduino Microcontroller – On the transmitting side, Arduino is used to process head movement data received from the MPU6050 sensor. It calculates the direction of movement based on tilt angles and then sends the corresponding control command to the Zigbee transmitter, which wirelessly forwards it to the wheelchair.

         Zigbee (Tx) Module – In the transmitting side, Zigbee is connected to the Arduino, which processes head movement data from the MPU6050 sensor. Once a movement is detected and converted into a control command, the Arduino sends this command to the Zigbee transmitter module, which wirelessly sends it to the receiver on the wheelchair.

      3. Receiver Module

         The receiver module is positioned remotely to receive data from the transmitter module and display the results.

         head movement commands wirelessly to the receiver. The Zigbee receiver passes these commands to the Arduino, which controls the wheelchair's motors for movement. It ensures real-time response with low power usage. This setup allows smooth and hands-free wheelchair navigation.

         Bluetooth Module – The Bluetooth module enables additional wireless communication, allowing external devices such as smartphones or assistive controllers to interact with the wheelchair system. This enhances accessibility and provides alternative control methods if needed.

         Ultrasonic Sensor – The ultrasonic sensor plays a crucial role in obstacle detection and collision avoidance. It continuously measures the distance between the wheelchair and nearby objects, sending real-time data to the Arduino. If an obstacle is detected within a predefined range, the system can trigger necessary actions, such as stopping or adjusting direction, to ensure user safety.

         Arduino Microcontroller – The Arduino serves as the central processing unit, receiving input signals from the Zigbee module, Bluetooth module, and ultrasonic sensor. It processes these inputs and sends appropriate control signals to the motor driver, determining the wheelchairs movement direction and speed.

         Motor Driver – The motor driver acts as an interface between the Arduino and the wheelchairs motors. It amplifies the control signals from the Arduino and supplies the necessary power to the motors, enabling smooth and efficient movement. The motor driver ensures precise execution of forward, backward, left, and right motions based on the received commands.

      4. System Advantages

   The proposed system offers several key advantages over traditional and existing systems:

   Hands-Free Operation The head motion-controlled system allows individuals with quadriplegia or severe motor impairments to navigate a wheelchair without using their hands or arms. This greatly improves accessibility for users who cannot operate traditional joystick-controlled wheelchairs.

   Emergency Stop Feature A built-in emergency stop mechanism allows the user or a caregiver to instantly halt the wheelchair's movement in case of an emergency. This is especially crucial when traveling on slopes, uneven surfaces, or in high-traffic areas.

   Customizable Sensitivity The system offers adjustable sensitivity settings to adapt to users with different levels of head movement ability. Whether a user has limited neck control or strong head movements, the system can be calibrated to ensure accurate and comfortable navigation.

2. METHODOLOGY

   The Head Motion Controlled Wheelchair is designed to provide a hands-free mobility solution for individuals with quadriplegia and severe motor imairments. The methodology involves hardware integration, signal processing, motion control, and safety features, ensuring seamless operation and user convenience [9]. The overall system consists of head motion detection, microcontroller- based processing, motor control, IoT monitoring, and obstacle detection. The step-by-step methodology is outlined below:

   Head Motion Detection Using MEMS Sensor-

   The system utilizes an MPU6050 MEMS sensor, which consists of an accelerometer and gyroscope to detect head movements in four directions:

   Forward tilt Move wheelchair forward Backward tilt Move wheelchair backward Left tilt Turn wheelchair left

   Right tilt Turn wheelchair right

   The MPU6050 sensor continuously monitors the user's head movements and transmits real-time motion data to the microcontroller for processing [10].

   Signal Processing Using Arduino Uno-

   An Arduino Uno microcontroller is used to process signals received from the MEMS sensor. The Arduino reads the tilt values and determines the required direction of movement. The data is filtered and processed to eliminate false readings caused by minor or unintended head movements [11]. The processed signal is converted into control commands for motor operation. A predefined threshold ensures that only intentional head movements trigger wheelchair motion.

   The system ensures real-time response with minimal delay for smooth navigation.

   Motor Control and Wheelchair Navigation-

   The wheelchair's movement is controlled by DC motors, which are operated using an L298N motor driver module. The Arduino sends movement commands to the motor driver, which adjusts the speed and direction of the motors accordingly.

   Forward command Both wheels move forward. Backward command Both wheels move backward.

   Left command Right wheel moves forward, left wheel remains slow or stops, causing a left turn.

   Right command Left wheel moves forward, right wheel remains slow or stops, causing a right turn.

   A pulse width modulation (PWM) technique is used to control motor speed and ensure smooth acceleration and deceleration [12].

   Obstacle Detection for Safety-

   To prevent collisions and ensure user safety, ultrasonic sensors are integrated into the system. These sensors continuously scan the surroundings and detect obstacles at a predefined distance. If an obstacle is detected within a critical range, the system automatically stops the wheelchair. An alert is triggered to notify the user or caregiver through the IoT module. This feature enhances indoor and outdoor navigation by preventing accidental collisions.

   IoT-Based Remote Monitoring and Emergency Alerts-

   An IoT module (ESP8266 or similar Wi-Fi module) is integrated to enable remote monitoring and real-time assistance [13]. The IoT system provides the following functionalities:

   location tracking Allows caregivers to monitor the wheelchair's movement in real time.

   Battery status updates Notifies users or caregivers when battery levels are low.

   Emergency alerts Sends an alert in case of system failure, sudden stops, or obstacles blocking the path.

   Remote control access Caregivers can stop or assist in wheelchair navigation remotely if needed.

   This IoT functionality ensures enhanced security, safety, and better user supervision.

   Emergency Stop Mechanism-

   To prevent accidental movement or system malfunctions, an emergency stop mechanism is integrated into the system.

   A manual emergency stop button is provided for the user or caregiver to instantly halt movement in case of emergencies. If an obstacle is detected too close, the system automatically stops the wheelchair to prevent collisions. The wheelchair remains stopped until the user releases the emergency stop mode or a caregiver intervenes remotely.

   Power Supply and Energy Efficiency-

   The system is powered by a rechargeable battery that ensures long operational hours without frequent charging. The power management system is optimized to:

   Reduce unnecessary power consumption when the wheelchair is idle. Extend battery life through efficient motor control and sensor operation. Provide alerts when battery levels are low, ensuring uninterrupted mobility.

   System Calibration and Testing-

   Before deployment, the system undergoes thorough calibration and testing to ensure accurate and reliable performance. The MEMS sensor is calibrated to detect

   intentional head movements accurately. Threshold values are adjusted to minimize false triggers and improve response time. The motor response is fine-tuned for smooth acceleration, braking, and turning.

   The obstacle detection system is tested to ensure quick reaction to obstacles. The IoT connectivity is verified for seamless real-time monitoring and alerts. Final testing is conducted in real-world scenarios to assess performance, usability, and safety before practical implementation.

3. SYSTEM DESIGN AND ARCHITECTURE

The transmitter module is a critical part of the head motion- controlled wheelchair, responsible for detecting and transmitting head movement data. It consists of the following components:

Figure 1 : Block Diagram of Transmitter Module.

The diagram represents the transmitter module of the head motion-controlled wheelchair system. This module is primarily responsible for detecting the user's head movements and wirelessly transmitting corresponding directional commands to the receiver module.

At the core of this module is the Arduino Uno, which acts as the central processing unit. It receives real-time motion data from a MEMS sensor (MPU6050), which captures head tilt in three axesX (left-right), Y (forward-backward), and Z (rotational movement). These movements are mapped to specific wheelchair directions (e.g., tilt forward = move forward).

The Arduino continuously processes these inputs and converts them into digital control signals. These signals are sent to the Zigbee transmitter (Tx), which wirelessly communicates the movement instructions to the receiver module located on the wheelchair.

To ensure uninterrupted functionality, the entire module is powered through a regulated power supply, providing the necessary voltage and current to the Arduino and other components. This configuration allows quadriplegic users to navigate a wheelchair using only their head movements, making the system both efficient and accessible, while maintaining low power consumption and wireless flexibility.

Figure 2 : Block Diagram of Receiver Module.

The receiver module is an essential part of the head motion- controlled wheelchair system, responsible for receiving transmitted signals, processing them, and executing the

corresponding movement commands. It consists of the following key components:

Calibrate MEMS Sensor:

Read initial accelerometer and gyroscope values to establish a baseline. Perform sensor calibration to minimize drift and noise.

Read Real-Time Sensor Data:

Continuously acquire accelerometer (,, )

and gyroscope (, , ) readings.Compute tilt angles using accelerometer data:

(

=1 ) .1

2 +2

The diagram above represents the receiver module of the head motion-controlled wheelchair system. This module is

=1 ) .2

(

2 +2

responsible for receiving commands transmitted from the transmitter module and executing them to drive the wheelchair accordingly.

At the center of this setup is the Arduino, which serves as the main controller. It receives directional data via the Zigbee Receiver (Rx), which wirelessly captures signals sent from the transmitter side based on the users head motion. This data is interpreted by the Arduino to determine the required movement directionsuch as forward, backward, left, or right.

The Bluetooth module is included to allow externaldevices, such as a smartphone or caregiver controller, to communicate with the Arduino for manual override or monitoring. Additionally, an ultrasonic sensor is integrated into the system to detect obstacles in the wheelchairs path. This sensor provides real-time distance measurements, helping the Arduino to prevent collisions by halting or adjusting movement when necessary.

The Arduino sends motor control signals to a motor driver circuit, which acts as an interface between the low-power control signals and the high-power DC motors that physically move the wheelchair. These motors are mounted on the wheelchair platform, allowing it to respond accurately to directional commands.

Power for the entire module is drawn from a battery, which is regulated through a power supply unit to ensure stable voltage to all components. This configuration allows for seamless and intelligent wheelchair control, enhancing mobility and safety for users with severe physical disabilities.

1. ALGORITHM STEPS

   Initialize System Components:

   Power on the system and initialize the Arduino, MEMS sensor (MPU6050), and Zigbee module. Set up communication protocols (I2C for MEMS sensor, serial communication for Zigbee).Define threshold angles for head movement detection.

   Integrate gyroscope data over time to estimate angular displacement:

   = . Apply a complementary filter to combine sensor readings for improved accuracy:

   = ( + ) + (1 )

   .3

   Determine Head Movement Direction:

   Compare the calculated tilt angles against predefined thresholds.

   If > , generate a forward movement command.

   If < , generate a backward movement command.

   If < , generate a right turn command.

   If < , generate a left turn command. Transmit Data to Receiver Module:

   Encode the movement command into a serial data packet. Send the data wirelessly through the Zigbee (Tx) module.

   Monitor and Adjust for Sensor Drift:

   Regularly recalibrate sensor readings to prevent drift over extended usage. Adjust sensitivity if needed to enhance precision.

   Loop Continuously:

   Repeat steps 3-6 in a continuous loop to ensure real-time wheelchair control. If the power is turned off, safely terminate all processes.

2. RESULTS AND DISCUSSION

   The implementation of the head motion-controlled wheelchair was rigorously tested to evaluate its efficiency, accuracy, and responsiveness. The system successfully translated head movements into directional commands, allowing users with quadriplegia to navigate their environment independently. The following aspects were analyzed in detail to assess the performance of the developed prototype:

   1. Accuracy of Head Movement Detection

      The MEMS sensor (MPU6050) demonstrated high accuracy in detecting head tilts and converting them into directional commands. The calculated tilt angles using the accelerometer and gyroscope data were compared with predefined threshold values, ensuring reliable movement control. The complementary filter applied to the sensor data significantly reduced noise and improved precision. The tilt angle measurements were validated using an external inclinometer, with an observed error margin of less than ±2 degrees, which confirms the reliability of the sensor-based detection system.

   2. Response Time Analysis

      The system's response time was a critical factor in determining its usability. The time delay between head movement detection and wheelchair movement was measured, and the results indicated an average delay of 200 300 milliseconds, which is within an acceptable range for real-time applications. The use of Zigbee wireless transmission ensured low latency communication, making the system responsive and efficient in executing movement commands.

   3. Stability and Sensor Drift Compensation

   One of the challenges in MEMS-based motion detection is sensor drift over time. During long-term testing, gyroscope drift was observed, leading to minor deviations in calculated angles. However, periodic recalibration and filtering techniques significantly reduced these deviations. The system successfully maintained stable operation over extended periods without requiring frequent recalibration, making it suitable for practical applications.

   Table 1: Comparison between Joystick Controlled and Head Motion Controlled Wheelchair [14].

   Sr No.	Comparis on Parameter	Joystick Controlled Wheelchai r	Head Motion Controlled Wheelchair	Remarks
   1	Control Method	Hand- operated joystick	Head motion via MEMS sensor	Suitable for quadriplegi cs
   2	User Dependen -cy	Requires hand and arm movement	No hand movement required	Improves user independen ce
   3	Response Time	Fast (~150 ms)	Moderate (~250 ms)	Slight delay due to processing
   4	Ease of Use	Requires motor coordinati on	Minimal physical effort	More accessible for disabled users
   5	System Cost	Medium to high	Low to medium	Cost- effective alternative
   6	Power Consumpt ion	Higher due to complex systems	Lower due to minimal components	More energy- efficient
   7	Adaptabili ty	Limited to physically able users	Customizab le for various disabilities	Highly adaptive
   8	Safety Features	Basic collision protection	Obstacle detection and emergency stop	Better safety features
   9	Technolo gy Complexit y	Relatively simple	Moderate with sensor integration	Requires basic technical setup
   10	User Comfort	Can cause fatigue over time	Comfortabl e for long- term use	Enhanced user experience

   quadriplegia to navigate their surroundings independently. With further advancements and refinements, this innovation has the potential to revolutionize mobility assistance, significantly enhancing the quality of life for individuals with severe disabilities. The study highlights the importance of leveraging modern sensor technology and wireless communication to create inclusive, intelligent, and practical assistive devices that cater to the needs of differently-abled individuals.

   Figure 3 : graphical comparison of wheelchair control method.

3. CONCLUSION

The development of the head motion-controlled wheelchair presents a significant advancement in assistive technology, offering an innovative, cost-effective, and user-friendly solution for individuals with quadriplegia and other severe motor impairments. Traditional wheelchairs, whether manual or joystick-operated, pose challenges for users with limited hand and arm mobility. The proposed system overcomes these barriers by utilizing a MEMS sensor (MPU6050) to detect head movements, which are then processed by an Arduino Uno and transmitted via a Zigbee module to control the wheelchair's movement efficiently. The system's ability to translate simple head tilts into precise directonal commands enhances the independence and mobility of users, making daily navigation more accessible and intuitive.

Extensive testing demonstrated that the system exhibits high accuracy in motion detection, minimal response delays, and reliable communication, ensuring smooth and stable operation. The integration of real-time signal processing and wireless control enables users to maneuver the wheelchair seamlessly in various environments. Furthermore, the power consumption analysis confirmed the system's efficiency, allowing extended usage without frequent battery recharges. While some challenges, such as sensor drift and sensitivity adjustments, were noted, these can be addressed through software-based calibration techniques and adaptive learning algorithms.

Compared to existing assistive mobility solutions, this head motion-controlled wheelchair stands out due to its affordability, ease of implementation, and non-intrusive nature. Unlike expensive brain-computer interface (BCI) systems or invasive control mechanisms, this design provides a practical and scalable solution that can be easily adopted by individuals with mobility impairments. Future improvements could include machine learning-based motion recognition, integration with smart home automation, and enhanced obstacle detection mechanisms to further improve safety and usability.

In conclusion, the proposed system bridges the gap between technology and accessibility, empowering individuals with

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