Publications

Authors:

Martini, Enrico; Boldo, Michele; Aldegheri, Stefano; Vale', Nicola; Filippetti, Mirko; Smania, Nicola; Bertucco, Matteo; Picelli, Alessandro; Bombieri, Nicola

Title:

Enabling gait analysis in the telemedicine practice through portable and accurate 3D human pose estimation

Year:

2022

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Format:

Elettronico

Referee:

Sì

Name of journal:

Computer Methods and Programs in Biomedicine

ISSN of journal:

0169-2607

N° Volume:

225

Number or Folder:

107016

Page numbers:

1-12

Keyword:

Marker-less gait analysis; 3D Human pose estimation; Edge computing; Telemedicine

Short description of contents:

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.

Web page:

https://doi.org/10.1016/j.cmpb.2022.107016

Product ID:

127746

Handle IRIS:

11562/1070651

Last Modified:

February 2, 2025

Bibliographic citation:

Martini, Enrico; Boldo, Michele; Aldegheri, Stefano; Vale', Nicola; Filippetti, Mirko; Smania, Nicola; Bertucco, Matteo; Picelli, Alessandro; Bombieri, Nicola, Enabling gait analysis in the telemedicine practice through portable and accurate 3D human pose estimation «Computer Methods and Programs in Biomedicine» , vol. 225 , n. 107016 , 2022 , pp. 1-12

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