Enabling Gait Analysis in the Telemedicine Practice through Portable and Accurate 3D Human Pose Estimation
Enrico Martini, Michele Boldo, Stefano Aldegheri, Nicola Valè, Mirko Filippetti, Nicola Smania, Matteo Bertucco, Alessandro Picelli, Nicola Bombieri
Abstract
Human pose estimation (HPE) through deep learning-based software applications is a trend topic for markerless motion analysis . Thanks to the accuracy of the state-of-the-art technology, HPE could enable gait analysis in the telemedicine practice. On the other hand, delivering such a service at a distance requires the system to satisfy multiple and different constraints like accuracy, portability, real-time, and privacy compliance at the same time. Existing solutions either guarantee accuracy and real-time (e.g., the widespread OpenPose software on well-equipped computing platforms) or portability and data privacy (e.g., light convolutional neural networks on mobile phones). We propose a portable and low-cost platform that implements real-time and accurate 3D HPE through an embedded software on a low-power off-the-shelf computing device that guarantees privacy by default and by design. We present an extended evaluation of both accuracy and performance of the proposed solution conducted with a marker-based motion capture system (i.e., Vicon) as ground truth. The results show that the platform achieves real-time performance and high-accuracy with a deviation below the error tolerance when compared to the marker-based motion capture system (e.g., less than an error of 5 ∘ on the estimated knee flexion difference on the entire gait cycle and correlation 0.91 < ρ < 0.99 ). We provide a proof-of-concept study, showing that such portable technology, considering the limited discrepancies with respect to the marker-based motion capture system and its working tolerance, could be used for gait analysis at a distance without leading to different clinical interpretation.