Stroke walking and balance characteristics via principal component analysis.

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Abstract

[Indexed for MEDLINE] Conflict of interest statement: The authors declare no competing interests. 9. Int J Environ Res Public Health. 2022 Aug 14;19(16):10032. doi: 10.3390/ijerph191610032. SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis. Sipari D(1), Chaparro-Rico BDM(2), Cafolla D(2). Author information: (1)Department of Control and Computer Engineering, Mechatronic Engineering, Politecnico di Torino, 10129 Torino, Italy. (2)Biomechatronics Lab, IRCCS Neuromed, 86077 Pozzilli, Italy. 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. DOI: 10.3390/ijerph191610032 PMCID: PMC9408480