# Markerless Pose Estimation

Finger-movement labels in non-human primates have long come from instrumented manipulanda fitted with bend sensors, which cap the dimensionality of measurable movement. Freebie uses several synchronized cameras and real-time pose estimation to output finger keypoints directly, reaching median end-to-end latencies of 15.8 ms at 30 FPS and 16.2 ms at 60 FPS, with a median absolute correlation of 0.96 against bend-sensor signals. EMG decoders trained on Freebie labels matched those trained on bend-sensor labels in closed-loop control, indicating that training a decoder need not require instrumenting the hand.

Finger-movement labels in non-human primates have long come from instrumented manipulanda fitted with bend sensors, which cap the dimensionality of measurable movement. Freebie uses several synchronized cameras and real-time pose estimation to output finger keypoints directly, reaching median end-to-end latencies of 15.8 ms at 30 FPS and 16.2 ms at 60 FPS, with a median absolute correlation of 0.96 against bend-sensor signals. EMG decoders trained on Freebie labels matched those trained on bend-sensor labels in closed-loop control, indicating that training a decoder need not require instrumenting the hand.