IMU-based Human Movement Recognition Using AI 

Movement recognition technology offers new
possibilities for enhancing dance practice,
education, and accessibility by enabling
real-time feedback, analysis, and
documentation. Wearable Inertial
Measurement Unit (IMU) sensors provide an
affordable, portable alternative to
camera-based systems, capturing motion data
directly from the body. This multichannel time
series data can be analyzed to recognize and
differentiate movement. To classify these
signals efficiently, we apply Minimally Random
Convolutional Kernel Transform
(MINIROCKET)—a fast, lightweight, and
accurate time-series classification method.
Our goal is to develop a low-cost, real-time
movement recognition tool that is adaptable
to the demands of live performance and
creative practice.