uLabel: The Enhanced LASK4 System

5-finger Machine Learning Labeling Device for Supervised Learning.

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The uLabel, formerly known as the LASK4 system, represents a significant evolution in hand movement and force measurement technology. This project is a joint endeavor between Ultimate Robotics and Open Muscle, showcasing our commitment to advancing somatosensory devices.

Introduction: The uLabel system, an evolution of the LASK4 device, is a groundbreaking project jointly developed by Ultimate Robotics and Open Muscle. It's designed to measure the movement and force of all five fingers, expanding its capabilities beyond the original four-finger design.

Key Features:

  • Five-Finger Measurement: Enhanced from the LASK4's four-finger model, uLabel accurately measures all five fingers.
  • Somatosensory Feedback: As a somatosensory device, it provides real-time feedback for hand rehabilitation, enhancing the user's recovery journey.
  • D'Addario Finger Training Integration: Maintains the use of the D'Addario finger training device, ensuring consistent quality and user experience.
  • Customizable Components: Comes with custom 3D STL file add-ons and a specially designed PCB for precise movement and force measurement.
  • Machine Learning Applications: Ideal for feature data labeling in machine learning, it plays a crucial role in AI research and development.

Technical Specifications:

  • Connectivity: The uLabel system utilizes UDP packets via the ESP32-S2 for transmitting sensor data to an accompanying computer.
  • Software: The system operates on micropython, embracing open-source and open-hardware principles. This facilitates widespread innovation and adaptability, allowing others to build upon and enhance the design.
  • Versatility: Initially aimed at hand rehabilitation, uLabel's potential extends to labeling training data for the Open Muscle Band in the prosthetics biometric sensor domain. Its adaptable design hints at future applications for measuring movement and force in other body parts.

Explore More: For a deeper insight into uLabel's capabilities in machine learning and hand rehabilitation, check out our latest video featuring the system in action. Stay updated with our project's progress and access all necessary resources on our GitHub page.

 The device was designed to label the training data accumulated by theOpen Muscle Band but as been designed to work with uMyo by Ultimate Robotics as well as any other forearm muscle sensing device.

Check out the latest video showing the LASK system being used in machine learning for open muscle!

LASK4 Github

The uLabel Version Zero prototype was sponsored by PCBWay!

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Live demonstration of finger predictions with actual values

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  • 1 × ESP32-S2 Mini by Wemos
  • 1 × D'Addario Hand Exerciser 4 Piston Spring Grip
  • 1 × 128x32 OLED SSD1306 OLED Screen
  • 1 × MakerFocus 3.7 Charge Circuit With protection
  • 4 × Hall Effect Sensor 49E SS49E Linear Hall Sensor

View all 10 components

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

    TURFPTAx05/02/2023 at 16:03 0 comments

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

    TURFPTAx04/16/2023 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.


    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.

    Prototype Version 5.3.0 with the Clevis Pin Piston. Sensor consist of a hall effect sensor on a PCB, a clevis pin holder made of PLA. A magnet...
    Read more »

  • LASK Successful Use in Machine Learning

    TURFPTAx03/29/2023 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

    TURFPTAx03/14/2023 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.

View all 4 project logs

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Shervin Emami wrote 03/30/2023 at 03:45 point

Nice project! The way you're measuring finger pressure using a PCB with guitar finger levers is interesting. I used a very similar guitar training device for a somewhat similar project (at My method with magnets & hall effect sensord doesn't require a custom PCB and custom 3D print, but it requires very delicate solder hacking skills! So your method seems better for mass production than my method 👌

I like your idea of using it to train a Myo muscle prediction algorithm, I hadn't thought about a project using that, so I'll be interested to see how your project goes. I plan on eventually building more gadgets for handicapped people.

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TURFPTAx wrote 03/30/2023 at 16:32 point

Oh very nice! I did not see your project before I started mine otherwise I might have used yours instead. I am able to detect finger movements and force with great accuracy with minimal ML training. The results are scary good actually. I'm posting a video update that shows the data training live and live predictions. The video should be 15 min and show the real-time ML training and prediction.

I'm going to check out your device too, I had someone reach out to me that needs a device with high sensitivity for spinal injury patients with limited finger movement. The springs in my system are too strong and I see the need for a super sensitive version in the near future. Maybe we could collaborate since we have similar visions?

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Shervin Emami wrote 04/01/2023 at 00:18 point

Yeah it's great that you're getting good results with just little training data!

Good point that there could be things we can collaborate on. I often do build weird human-computer-interfaces, with a long term vision of doing them specifically to assist disabled people in 3rd world countries. For now I've taken a break from focusing on others and I'm focusing on myself more, but also with some thought around how it can be useful to others in the future.

For example this month I'm currently building a glove that let's me play drums while driving on the highway, by tapping my fingers on the steering wheel. It's designed for me to have fun, but after I've got that mechanism working well for playing drums, I'll later adjust the code so it can be used as an alternative computer interface for people with RSI for example.

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z26 wrote 03/14/2023 at 10:40 point

I find this project interesting for a completely different usecase: the company wooting released an analog keyboard that can be used in gaming to simulate a joystick of for pressing a button really fast (like in the rhythm game Osu) but its quite expensive and there's a long order wait list.

Wooting actually sells their magnetic switches (with no sensor) on their website, so in theory one could make a small keypad that has enough keys for gaming purpose for much cheaper.
theres even a tutorial for it

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