Project Logs

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• Video Update on LASK and AI Training Use for Prosthetic Sensors

  TURFPTAx • 05/02/2023 at 16:03 • 0 comments

• LASK as a Machine Learning Labeler to Successfully Detect Finger Curls

  TURFPTAx • 04/16/2023 at 16:18 • 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 »

• LASK Successful Use in Machine Learning

  TURFPTAx • 03/29/2023 at 12:22 • 0 comments

  We were exited to post our preliminary tests with the LASK4v2 and OM12.

  Since the LASK system is relatively simple and the data is clean it works great for machine learning.

  Here you see the LASK being used to train and to verify the predictions of a machine learning model.
  

• LASK4 Version 2 Updates

  TURFPTAx • 03/14/2023 at 18:00 • 0 comments

  LASK4v2 Works and Charges the Battery this time!
  The signals are clean and the micropython code works well. Started my initial data acquisition for open muscle using the LASK4 as the main controller.

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