FLEX A MUSCLE AND THE HAND OBEYS — YOU CAN 3D PRINT THIS BIONIC HAND
A 3D-printed prosthetic hand that reads muscle signals and mirrors your grip — research-grade bionic you can build from common parts.
by Patrick Slade and the Bretl Lab
Open-hardwareHealth
- difficulty
- ●●●●●
- time
- several weekends
- license
- Unlicense
- repo
- repo ACTIVE28 stars
●●●●● · several weekends · Unlicense · 28 stars · repo ACTIVE
WHAT YOU’LL NEED
- 3D printer + filament — printable parts — files are in the repo
- Dev board / microcontroller — runs the project firmware
Partner
COMPAREE VERDICT
Tact is a myoelectric prosthetic hand from the Bretl Lab at University of Illinois — EMG electrodes on your forearm read muscle contractions, an Arduino classifies the intended grip, and five servo motors move the fingers. It is one of the few open prosthetic designs that goes beyond a simple mechanical gripper into real signal processing. The repository contains the STLs, the Arduino firmware, and a 67-page thesis that walks through the electronics and machine learning. What it does not contain is a BOM or a build manual. You will spend time cross-referencing the thesis with the code to figure out which EMG sensor board, which servos, which power supply. The EMG signal chain is the hardest part — electrodes need skin prep, placement matters, and the classifier needs training samples from the user. If you have never worked with biopotential signals before, expect a steep learning curve. The hand itself prints in PLA and assembles with off-the-shelf servos, so the mechanical side is straightforward. This is a research artifact, not a kit, and it shows — but if you want to understand how myoelectric control actually works, this is one of the few open projects that gets you there. The team went on to found PSYONIC, whose current product is closed and costs five figures, so this repository is the only open window into that lineage. One major limitation: this is a right hand only, and adapting it for a left hand or a different forearm size will require CAD work.
IN THE REPO
NOT IN THE REPO
- —All STL files for printing the hand and forearm are in the repository.
- —Arduino code for EMG signal processing and motor control is included.
- —A master's thesis PDF documents the design, but there is no step-by-step build guide.
- —No bill of materials — you extract the parts list from the thesis and code comments.
- —Released under GPL-3.0, which permits commercial use but requires derivative work to remain open.
- —This is a 2016 research prototype; the team later founded PSYONIC and moved to a closed commercial product.
Can I build this?
Build at your own risk. Projects involve tools, electronics and sometimes mains voltage — follow the creator’s safety notes.
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Start here
Navigation into the creator’s own docs — we don’t rewrite the guide, we route you to the source.
- 1.Read the thesis PDF in the repository — it is the only complete description of the electronics and signal processing. (The thesis is TactileFeedbackThesis.pdf in the root. Pages 20-35 cover the hardware.)
- 2.Extract the parts list from the thesis and code comments — there is no separate BOM.(The code references pin assignments and sensor types, the thesis mentions servo models. Cross-reference both.)
- 3.Print the STL files in the CAD folder — hand parts first, then the forearm socket.(All models are right-handed. PLA works; PETG is more durable for the finger joints.)
- 4.Assemble the hand and test the servos with a simple Arduino sketch before connecting the EMG board.(Get the mechanical side working first — the EMG layer adds complexity.)
- 5.Connect the EMG sensors and run the calibration routine in the firmware to train the classifier.(Electrode placement and skin prep are critical — the thesis has a diagram on page 28.)
KNOWN ISSUES
- There is no bill of materials — you have to piece together the electronics from the thesis and code comments, and some part numbers are not specified.
- EMG sensors are finicky — dry skin, poor electrode contact, or nearby electrical noise will give you garbage signals. Budget time to learn proper placement and skin prep.
- The classifier is trained for the original user — you will almost certainly need to retrain it with your own muscle signals, which means understanding the training routine in the code.
- The repository is from 2016 and has not been updated — libraries and Arduino APIs have changed, so expect some code adjustments.
- This is a right hand only — mirroring the STLs for a left hand requires CAD skills, and the forearm socket is not parametric so it fits one size.
- The team moved on to a commercial closed product (PSYONIC) — do not expect support or active development on this repository.
Can I use this if I am not an amputee?
Yes — the EMG sensors work on any forearm muscles. Many builders use it as a research platform or demonstration without a prosthetic application. You will still need to train the classifier with your own muscle signals.
What EMG sensor board should I buy?
The thesis references Advancer Technologies EMG sensors, but those are hard to source now. Myoware sensors from Sparkfun or Adafruit are a common modern substitute — you may need to adjust the analog input scaling in the code.
How many grips can it do?
The firmware classifies four grip patterns: cylindrical, tip, hook, and spherical. You can add more by retraining the classifier, but that requires collecting new EMG samples and editing the training code.
Is there tactile feedback like the name suggests?
The thesis explored vibrotactile feedback on the forearm, but that code is not in the public repository — the current release is motor control only.
Community builds
No community builds yet — be the first, we feature the best ones.
Discussion1
FROM THE COMPAREE TEAM
The thesis documents four grip patterns, but the real question is electrode placement — where on your forearm did you get the cleanest EMG signal?
Patrick Slade and the Bretl Lab
Patrick Slade developed Tact as part of his master's research at the University of Illinois under Professor Timothy Bretl. The project explored affordable myoelectric control for upper-limb prosthetics. The lab later spun out into PSYONIC, a company that now makes a commercial bionic hand, but the original research prototype remains open under GPL-3.0.
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- Information is provided without warranty and may become outdated as projects evolve. Prices are indicative bands only — always check the creator’s parts list for current costs.
- Building and operating any project is at your own responsibility. Protective equipment, safe workshop practice and compliance with local regulations are the builder’s responsibility.
CompareeTEAM1mo agoedited
Practical notes from our verification: this repository has not been touched since 2016, the original Advancer Technologies EMG sensors are no longer sold, and there is no official build video — all the demo footage is from conference presentations. The biggest time sink is not the printing or assembly, it is getting stable EMG readings and retraining the classifier for your own arm. The thesis PDF is excellent documentation for a research paper, but it is not a build manual — you will cross-reference it with the code to figure out wiring. If you have never worked with biopotential sensors before, start with a Myoware breakout and a simple LED blink sketch to understand signal conditioning before you tackle the full hand.