Department of Control and Computer Engineering, Mechatronic Engineering, Politecnico di Torino, 10129 Torino, Italy.
Biomechatronics Lab, IRCCS Neuromed, 86077 Pozzilli, Italy.
Int J Environ Res Public Health. 2022 Aug 14;19(16):10032. doi: 10.3390/ijerph191610032.
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.
人类的步态周期可能受到多种因素的影响，包括神经、骨科和病理状况。因此，步态分析具有广泛的应用，包括神经障碍的诊断、疾病发展的研究、治疗效果的评估、姿势矫正以及运动表现的评估和增强。虽然新技术的引入带来了实质性的进步，但这些系统在成本、分析准确性、速度和便利性之间仍然难以取得平衡。目标是为运动障碍者提供低成本的支持，以提高他们的生活质量。本文提出了一种新的基于人工智能的自动化运动特征描述方法，用于实时分析、完全自动化和非侵入性、无标记分析。这种自动化程序可以实现快速诊断，防止人为错误。通过两种运动跟踪系统获得的步态指标进行了比较，以展示所提出方法的有效性。
