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description: MIT engineers have developed an ultrasound wristband that tracks hand movements in real-time by imaging wrist muscles and tendons. Paired with an AI algorithm,...
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# Wristband enables wearers to control a robotic hand with their own movements

**[Robohub](https://daily.dev/sources/robohub)** · 8 min read · 2 upvotes · 0 comments

## Summary

MIT engineers have developed an ultrasound wristband that tracks hand movements in real-time by imaging wrist muscles and tendons. Paired with an AI algorithm, the device maps 22 degrees of freedom in the hand and fingers, enabling wearers to wirelessly control a robotic hand or manipulate virtual objects. Demonstrations include playing piano via a robot and interacting with 3D objects on screen. The team tested the device on eight volunteers performing ASL signs and object grasps. Future plans include miniaturizing the hardware, expanding the training dataset, and building toward a universal wearable hand tracker for humanoid robots and VR applications.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://robohub.org/wristband-enables-wearers-to-control-a-robotic-hand-with-their-own-movements>

## Questions this post answers

### How does the MIT ultrasound wristband track hand movements to control a robotic hand?

It uses a smartwatch-sized ultrasound sticker embedded in a wristband to continuously image the muscles, tendons, and ligaments in the wrist as the hand moves. An AI algorithm trained on labeled ultrasound images correlates specific image regions with the hand's 22 degrees of freedom, translating wrist images into finger and palm positions that can wirelessly control a robotic hand or virtual object in real time.

_Developers exploring novel human-robot interfaces can find hands-on research writeups like this on daily.dev._

### Why is ultrasound imaging better than EMG sensors or camera-based systems for tracking hand gestures?

Ultrasound imaging captures more continuous and dexterous hand movement data than electromyography (EMG) signals from wrist muscles, which are noisy and struggle to distinguish subtle in-between finger positions like partial pinches. It also avoids the practical drawbacks of camera-based systems, which suffer from visual obstacles, and sensor-laden gloves, which can restrict natural hand motion and sensation.

_Anyone comparing sensing approaches for gesture control can track research like this on daily.dev._

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- [Ultrasound imaging turns a robot hand into a skillful mimic](https://daily.dev/posts/ultrasound-imaging-turns-a-robot-hand-into-a-skillful-mimic-z2shjikij) · MIT Technology Review · 0 upvotes · 0 comments
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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#robotics](https://daily.dev/tags/robotics), [#human-computer-interaction](https://daily.dev/tags/human-computer-interaction)

[View this post on daily.dev](https://daily.dev/posts/wristband-enables-wearers-to-control-a-robotic-hand-with-their-own-movements-em1i2rrf1)

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