Hardware Components:
- Arduino Nano Microcontroller: This small and powerful microcontroller is the system's brain. It reads sensor data, processes it, and controls the display output.
- Five Flex Sensors: These sensors are attached to the fingers of a glove. Flex sensors change their resistance when bent. The more a sensor is bent, the higher the resistance it outputs. These sensors provide real-time data on finger movement.
- 7-Segment 4-Digit Display: This is a digital display controlled by the Arduino Nano, used to show the player’s performance in the game. this display is used to show the movement of the four fingers ( index finger, middle finger, pinky finger)
- 7-Segment 1-Digiat Display: It is a common-cathode display where each digit is controlled individually, allowing it to show numbers between 0 and 9. These 7 segments present the movement of the Thumb.
Software Functionality:
- Sensor Data Acquisition: In the code, the analogRead function is used to capture the analog values from the four flex sensors connected to the analog pins (A0, A1, A2, and A3) of the Arduino Nano. These values represent the bending degree of each finger.
- Data Mapping: The analog values from each sensor are mapped to a digit (0-9) using the map function. This conversion helps in translating the flex sensor readings into understandable metrics for the game.
- Display Control: The TM1637Display library is used to interface with the 4-segment 4-digit display. The encoded digits are shown on the display, providing visual feedback on the movement accuracy of the fingers.
- Game Mechanism: Each digit on the display corresponds to the movement of a specific finger. The players can see their performance as they attempt to mimic certain finger positions or sequences required by the physiotherapy exercises.
Physiotherapy Application:
This glove game aids in physiotherapy by providing real-time feedback to patients as they perform hand exercises. Patients recovering from hand injuries or surgeries often need to perform specific finger movements to regain strength and dexterity. The glove captures the fine motor movements of the fingers and translates them into visual feedback.
The display shows how well the patient performs the exercise, encouraging them to improve their movements. This immediate feedback is crucial for engaging patients and ensuring they perform the exercises correctly. Over time, this can lead to faster recovery and better rehabilitation outcomes.
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