SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis

Dario Sipari, Betsy D. M. Chaparro-Rico and Daniele Cafolla ()
Additional contact information
Dario Sipari: Department of Control and Computer Engineering, Mechatronic Engineering, Politecnico di Torino, 10129 Torino, Italy
Betsy D. M. Chaparro-Rico: Biomechatronics Lab, IRCCS Neuromed, 86077 Pozzilli, Italy
Daniele Cafolla: Biomechatronics Lab, IRCCS Neuromed, 86077 Pozzilli, Italy

IJERPH, 2022, vol. 19, issue 16, 1-27

Abstract: The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology.

Keywords: human biomechanics; automated gait analysis; artificial intelligence; motion tracking; markerless (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/16/10032/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/16/10032/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:16:p:10032-:d:888103

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().