imu-weartime: IMU Wear-Time Detection

A Python library providing validated wear-time detection algorithms for lower-back worn IMU sensors. Includes both established methods from the literature and novel signal processing, machine learning, and deep learning approaches.

This library was developed as part of the SUSTAIN Mobilise-D project and implements algorithms designed to work within the MobGap ecosystem for mobility analysis.

Novel algorithms outperform all literature methods by 8- to 131-fold in wear-time quantification accuracy.

Software developed by Dr Dimitrios Megaritis. Scientific authorship is listed in CITATION.cff.

Funding and Support
This work was supported by the SUSTAIN Mobilise-D Consortium. Content in this publication reflects the authors' view and neither EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.