Project Logs

Collapse

• The Journey of Open Muscle: Democratizing Assistive Technology

  TURFPTAx • 07/13/2023 at 13:36 • 0 comments

  One day, while scrolling through the internet, I came across a video that stopped me in my tracks. It was about a man with no arms who had undergone surgery to move nerves into his pectoral muscles. This transformation enabled him to control two prosthetic limbs. But there was a catch – he couldn’t take these arms home. Why? Because the technology and code that powered them were proprietary. This man was denied the freedom to fully embrace these life-altering tools because of a man-made barrier. The story was not just sad; it was infuriating.

  This experience became the spark that ignited the creation of Open Muscle. I saw the potential of machine learning and believed that it could provide a solution. I had an idea: a matrix of pressure sensors sampling forearm muscle movements and using a force detection labeling system to measure finger movements. In theory, AI or machine learning could predict the finger movements based on the pressure sensors’ data.

  It was an ambitious plan, but I was ready to face the challenges head-on. The journey was not without its difficulties. For instance, finding the right springs for the pressure sensors was a task that felt like an endless Goldilocks conundrum. After much trial and error, the solution came in the form of a larger pressure pad, which allowed us to use firmer springs.

  Another challenge was designing a system that could be adjusted and upgraded if needed. The solution was a radial design, which captured sufficient data from the muscles and was a perfect fit for machine learning.

• Open Muscle Video Update AI Prosthetic Sensor

  TURFPTAx • 05/02/2023 at 16:06 • 0 comments

• Finger Movement Recognition Using Radial Pressure Sensors on the Forearm and Machine Learning Techniques

  TURFPTAx • 04/16/2023 at 16:14 • 0 comments

  In this study, we present a novel and cost-effective method for detecting individual finger movements using a custom pressure sensor array based on Hall effect sensors and magnets. Twelve sensors were placed radially around the forearm, and machine learning techniques, specifically a random forest regressor, were employed to distinguish between finger curls. With only 5 minutes of training data, our approach achieved a mean absolute error (MAE) of 35, demonstrating its potential as a viable and inexpensive sensor solution for finger movement and force detection. This forearm armlet-based system has applications in fields such as prosthetics, robotics, and human-computer interaction, offering a promising alternative to traditional sensor technologies.
  

  Introduction

  Hand amputations can significantly impact an individual’s quality of life and ability to perform daily tasks. Despite losing their hand, many people with amputations retain an intact forearm with functional muscle movement. However, due to the disconnected tendons, detecting and interpreting finger movements becomes challenging. In this study, we sought to develop an innovative and cost-effective sensor solution that could discern individual finger movements by sampling the force exerted by muscle contractions in the forearm. One of our main challenges was finding a low-pressure sensor system with minimal friction. We explored different designs and ultimately used a spring and clevis pin system for the best results, while also considering rubber foam for a more scalable solution in future designs.

  We hypothesized that with minimal training data, our approach could provide an inexpensive and accessible solution for a wide range of individuals who could potentially benefit from this technology. To test our hypothesis, we conducted experiments on able-bodied individuals with the intention of validating the efficacy of our approach and warranting further research in the context of hand amputees. Our work was conducted using open-source and open hardware principles to contribute to the scientific community with a non-proprietary alternative to existing forearm sensor technologies. This open approach aims to foster collaboration, innovation, and widespread adoption of our proposed forearm bracelet system.

  In this paper, we describe the design and implementation of our custom pressure sensor array, the machine learning techniques employed to recognize finger movements, and the performance of our system in detecting individual finger curls. We also discuss potential applications of our technology in prosthetics, robotics, and human-computer interaction, as well as future directions for research and development.

  Materials & Methods

  Pressure Sensors:

  Custom pressure sensors were designed using Hall Effect sensors mounted on printed circuit boards (PCBs). These PCBs were attached to PLA 3D printed clevis pin holders, which featured a hole for the clevis pin piston positioned 7mm above the Hall Effect sensor. A nail head “foot” was secured to the piston, and a 5mm diameter earth magnet was glued to the foot. The piston was fitted into the clevis pin holder, and a spring and clip were attached to the opposite side. The feet were designed to make contact with the skin, and their surface area could be adjusted to modify the sensor’s overall amplitude.

  A second version of the sensor utilized the same PCB but incorporated a rubber foam spring system, a rubber sheet, and a magnet. This alternative design yielded promising results and is recommended for further investigation, as its construction requires less time.

  Sensor Array and Data Acquisition: The magnet array was wired together and connected to an ESP32-S2 microcontroller, which utilized MicroPython firmware and its 20 13-bit ADCs to measure the Hall sensor values.

  Read more »

• More Live Prediction Tests

  TURFPTAx • 04/04/2023 at 12:56 • 0 comments

  The field of wearable technology and human-machine interfaces has made significant strides over the past few years. The development of ‘Open Muscle,’ an innovative system that utilizes 12 pressure sensors built with hall effect sensors and magnets with springs in between, has proven to be a game-changer in accurately predicting finger movements. This article explores the benefits of using a random forest regressor to enhance the capabilities of the ‘Open Muscle’ system, the advantages of the LASK system, and the potential for future hardware optimizations.

  The Power of Random Forest Regressors

  Random forest regressors, an ensemble learning technique, have been shown to yield exceptional results in a wide range of applications, from medical diagnosis to financial forecasting. In the context of predicting finger movements based on pressure sensor samples around the forearm, random forest regressors offer several key benefits:

  1. Robustness to noise: Given that the initial tests of the ‘Open Muscle’ system were conducted without applying any filters or hardware optimizations, the data collected might be noisy. Random forest regressors are known for their resilience to noise, which makes them an ideal choice for this application.
  2. Handling high-dimensional data: With 12 pressure sensors producing a large volume of data, random forest regressors can manage this high-dimensionality without compromising prediction accuracy.
  3. Reduced overfitting: By averaging multiple decision trees, random forest regressors are less prone to overfitting and can generalize better to new data.
  4. Feature importance: Random forests can rank the importance of features, helping researchers identify the most relevant pressure sensors and refine the hardware configuration accordingly.

  The LASK System: Enhancing Labeling and Efficiency

  The LASK system, which applies the labels to the pressure sensors, plays a crucial role in improving the ‘Open Muscle’ system’s performance. By using force measurements instead of movement alone, the LASK system provides a more reliable and accurate output for the machine learning model. This approach not only speeds up the training process but also enhances the overall efficiency of the system.

  Exploring Future Optimizations

  While the ‘Open Muscle’ system has shown promising initial results, there is significant potential for future improvements through hardware optimizations and the exploration of alternative machine learning algorithms. Some possible avenues for enhancement include:

  1. Implementing filters to minimize noise and improve signal quality.
  2. Optimizing hardware configurations based on feature importance from the random forest regressor.
  3. Comparing random forest regressors with other machine learning algorithms, such as support vector machines or neural networks, to evaluate performance and prediction accuracy.

• Detecting Finger Movement Live with only 5 min of trainig data

  TURFPTAx • 03/29/2023 at 00:36 • 0 comments

  I was able to get live predictions with only 5 min of training data with the sensor system.

  The accuracy is lower but I just wanted to make sure that I had done everything correct.

• Detecting Finger Movements with bracelet and Machine Learning

  TURFPTAx • 03/26/2023 at 23:38 • 0 comments

  We are excited to share our latest findings in predicting finger movement and pressure using machine learning. The results show that our model is capable of predicting the finger movement within a Mean Absolute Error (MAE) of 25, which is a sufficient level of accuracy for detecting both the finger movement and the pressure applied.

  These screenshots showcase a portion of the data file available for download, which contains the actual and predicted finger movement and pressure values. Our model not only indicates that a finger is moving but also estimates the amount of pressure being applied, providing valuable insights into the intricacies of finger movements.

  This achievement opens up new possibilities for applications that require precise finger movement and pressure detection, such as in rehabilitation therapy, robotics, and gesture-based user interfaces.

  
  

  We invite you to download the full data file and explore the results in more detail. As we continue to refine our model and improve its accuracy, we look forward to discovering new ways to utilize this technology for the betterment of various fields and industries.

  All data to train the model and code available on our Github: https://github.com/turfptax/openmuscle

• Random Forest Regressor Model Predicted Well

  TURFPTAx • 03/26/2023 at 22:08 • 0 comments

  The trained model had a Mean Average Error (MAE) value of approximately 25, equating to a 12% error rate. Despite the challenges, the model was able to make real-time predictions, demonstrating its potential for a significant impact on the prosthetic industry.

  GitHub Link for Open Muscle: https://github.com/turfptax/openmuscle

  predictions_vs_actuals2Download

• OM12 Updated: Replaced Clevis Pins with Foam!

  TURFPTAx • 03/14/2023 at 18:04 • 0 comments

  I recently replaced all of the springs and clevis pins with a foam solution that produces similar results. A lot of the force goes into the elastic band and the depth of the ADCs is very important. With the ESP32-S2 I am able to obtain 1200 samples per second across all sensors or 100 samples per second for each sensor.
  

  The resulting signals are not as 'loud' as I wished them to be but they are reading the movement of the muscles underneath the skin. Will be posting a video update with the pros/cons of this system for use in biometric muscle contraction detection of forearm muscles.

View all 8 project logs