News

We are excited to announce that our innovative finger tracking sensor, Open Muscle, has been submitted to the Supply Frame contest on hackaday.io. Open Muscle is a device designed to track finger movements, making it a valuable tool for building computer interface devices or controlling prosthetic hands. It consists of a wrist cuff (forearm band)… Read More »Open Muscle: Revolutionizing Assistive Technology with Finger Tracking Sensors & Machine Learning

After obtaining initial results, I conducted further studies on myself to ensure that the random forest regressor was not simply memorizing the data. I employed a method that involved starting two separate sessions for open muscle and LASK data capture, but only fed one of them into the model. During the training process, I kept… Read More »A Further Review of the Data

Abstract 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… Read More »Finger Movement Recognition Using Radial Pressure Sensors on the Forearm and Machine Learning Techniques

The Quest for the Perfect Material to Optimize Sensor Performance Introduction: Welcome to another exciting blog post on openmuscle.org, where we delve into the fascinating world of muscle-based finger movement sensor systems! Today, we will be discussing the current research and development efforts being made towards the next generation of the Open Muscle bracelet. This… Read More »Revolutionizing Human-Machine Interaction: The Next-Generation Open Muscle Bracelet

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… Read More »Random Forest Regressor and Pressure Sensors to Predict Finger Movement

Open Muscle, an innovative project aimed at detecting finger movement and pressure using a forearm bracelet, has recently achieved a remarkable milestone. By using just 5 minutes of training data gathered from the Open Muscle 12 and the LASK4 device, the team was able to train an AI model that successfully detects finger movement and… Read More »Detecting Finger Movement and Pressure with only 5 Minutes of Training Data: Open Muscle’s Breakthrough

The field of prosthetics has advanced tremendously in recent years, thanks to cutting-edge technology and the collaborative efforts of researchers and engineers worldwide. Open Muscle, an open-source and open hardware project, is at the forefront of this innovation, harnessing the power of machine learning to improve the lives of amputees and those with limited mobility.… Read More »Building a More Robust Model for Open Muscle: The Need for Diverse Training Data

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.… Read More »Predicting Finger Movement and Pressure with Machine Learning

The Open Muscle project aims to revolutionize prosthetic technologies by harnessing the power of artificial intelligence (AI) to predict muscle contractions. Open Muscle is a machine learning prosthetic sensor suite that uses 12 pressure sensors on a bracelet for feature data and a LASK system comprised of piston springs for finger movement label data. The… Read More »Harnessing AI to Revolutionize Prosthetic Technologies: Open Muscle & ChatGPT

In this article, we discuss the performance of a sensor system with 12 input features and a time-domain dataset, accompanied by a 4-sensor label output with time-domain values. To evaluate the model’s performance, we focus on the Mean Absolute Error (MAE) metric. We’ll explore the significance of the MAE values obtained and the implications for… Read More »Understanding and Assessing the Performance of a Sensor System with Mean Absolute Error