Video demo: Real-Time Robotic Hand Control via sEMG-Based Pose Estimation during Unconstrained Movements

This demo shows real-time control of a robotic hand (Leap Hand) from 8 sEMG sensors around the forearm using regression of the finger joint angles. The recognition model is first trained from 10 minutes of EMG data from the test subject, labeled using a Leap Motion camera.


System Overview

Figure 1. A. The MindRove 8-channel EMG armband system to record the sEMG signals. B. The Leap Motion Controller to estimate the hand poses. C. The Leap Hand robotic hand that represents the final physical effector. D. Synchronized sEMG signals with three representative finger joint angles.


Watch the Demo


Prediction Results

Figure 2. Visualization of the true (black) versus predicted (blue) finger joint angles over time.

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