V2.0 Fitness Assistant Based on Electromyography (EMG) Sensors

A year ago, I made a fitness assistant which uses EMG sensors placed onto the body to try to prevent improper form, which could lead to suboptimal results or even injuries for people who are training at home. This is an updated version, in which I used the same major components, but changed the outer appearance to one that takes up a smaller footprint, but also has space to hold the wires required for both the EMG pads and the connecting cables from the Arduino UNO to your computer. In conclusion, here is an updated version or essentially the same thing, with a different appearance and more utility. Because of this, all of the steps and supplies will be the same, bar the appearanc being different.

Here is the original introduction:

In modern society, fitness exercise has become an important part of people's daily lives. However, due to a lack of professional guidance, many individuals perform fitness training with improper form, leading to suboptimal training results and even exercise-related injuries. This issue is particularly severe in home fitness and self-training scenarios. To address this problem, this project designs and develops a fitness assistant device based on electromyography (EMG) sensors, aiming to help users ensure proper form and provide real-time feedback, thereby improving training effectiveness and safety. The device primarily includes the following functional modules: EMG signal acquisition and processing, adaptive motion standards, real-time feedback mechanisms, and convenient user interaction.

When users first use the device, they record the EMG signal values of standard movements by pressing a detection button, and the device automatically generates personalized motion standards. During training, the device continuously monitors the user's EMG signals and provides feedback through an LCD1206 display, prompting users to adjust their movements. To validate the effectiveness of the device, feasibility experiments, scientific experiments, and practical experiments were conducted. The results indicate that the fitness assistant device based on EMG sensors can accurately collect and process EMG signals, provide timely and accurate feedback, and significantly improve users' training effectiveness and satisfaction.

Therefore, the fitness assistant device based on EMG sensor technology can effectively solve the problem of improper form in fitness training. It offers advantages such as real-time feedback, personalized standards, and convenient interaction, making it highly significant and valuable for application in home fitness and self-training contexts.

1. **Microcontroller (Arduino UNO)**: Responsible for processing the collected EMG signals and controlling other hardware modules.

2. **EMG Sensor Module**: Used to collect electrical signals from muscle activity.

3. **LCD1206 Display**: Provides real-time feedback information, prompting the user to adjust their actions.

4. **Detection Button**: Used for user operation, pressing the button starts the detection process.

5. **RGB Lights**: Six aligned RGB lights can provide feedback on the user's exertion levels.

6. ** Buzzer**: Provides sound prompts for the start and end of operations.

6. **Power Module**: Supplies the necessary power for the entire device.

7. **3mm Basswood Board + Transparent Acrylic Board**: Used for cutting and making the enclosure.

The hardware setup is the foundation of the entire project, ensuring correct connections between components is a prerequisite for functionality. Before starting the actual connections, I first drew a wiring diagram to ensure that the pin connections of each component are clear (below is the wiring diagram for the main components). Following the above wiring diagram, I completed the basic hardware connections, ensuring that each component can be powered and communicate properly.

After completing the hardware connections, we need to write the corresponding software code to achieve various functions. Software development includes modules for signal acquisition, processing, feedback display, and data recording.

1. Development Board: Arduino Uno
2. Programming Language: C++
3. Development Environment: Arduino IDE
4. Version: Arduino IDE 1.8.13

Our code mainly includes the following modules:

1. Initialization Module: Initializes various components, including the EMG sensor, LCD display, and buttons.
2. Signal Acquisition Module: Reads EMG signals from the EMG sensor and performs preliminary processing.
3. Standard Value Recording Module: Records the current EMG signal as the standard value when the user presses the detection button.
4. Real-time Monitoring Module: Monitors the EMG signal in real-time and compares it with the standard value after the user presses the start button.
5. Feedback Display Module: Provides feedback information through the LCD display based on real-time monitoring results.
6. Data Recording Module: Records the user's training data each time, including the number of actions and EMG signal strength.

Building on the initial design, I have made the appearance so that it looks more like a little robot, with a wooden antenna on its head. I laser cut all the pieces as before, and the laser cutting pattern is attached. The two parts on the sides function as space for holding the wires for the EMG sensors as well as the arduino to computer wire for transmitting the code. This takes advantage of the fact that there is a little excess space in the back of the circuitry, allowing a more compact design. The back is once again made of acrylic because I find it more aesthetic than covering up the circuitry completely.

Verify whether the fitness assistant based on EMG (electromyography) sensors can accurately collect EMG signals and process and provide feedback through the Arduino controller. Verify the accuracy and stability of the device under different conditions to ensure its scientific validity.

Experimental Steps:

1. Attach the EMG sensor to the abdominal muscles of different testers.

2. Start the device, press the detection button, and record the standard EMG signals of each tester.

3. Have different testers perform crunches or sit-ups, press the start button, and monitor the EMG signals in real-time.

4. Record the changes in EMG signals and feedback information of different testers, and analyze the accuracy and stability of the device.