Cerfoglio Serena, Ferraris Claudia, Vismara Luca, Amprimo Gianluca, Priano Lorenzo, Bigoni Matteo, Galli Manuela, Mauro Alessandro, Cimolin Veronica
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Division of Neurology and Neurorehabilitation - IRCCS Istituto Auxologico Italiano, Verbania, Italy.
Front Bioeng Biotechnol. 2024 Nov 25;12:1449680. doi: 10.3389/fbioe.2024.1449680. eCollection 2024.
Walking ability is essential for maintaining functional independence, but it can be impaired by conditions like hemiplegia resulting from a stroke event. In post-stroke populations, accurately assessing gait anomalies is crucial for rehabilitation to promote functional recovery, and to prevent falls or injuries.
The aim of this study is to evaluate gait-related parameters using a solution based on a single RGB-D camera, specifically Microsoft Azure Kinect DK (MAK), on a short walkway in both healthy (n= 27) and post-stroke individuals with hemiplegia (n= 20). The spatio-temporal and center of mass (CoM) parameters estimated by this approach were compared with those obtained from a gold standard motion capture (MoCap) system for instrumented 3D gait analysis.
The overall findings demonstrated high levels of accuracy (> 93%), and strong correlations (r > 0.9) between the parameters estimated by the two systems for both healthy and hemiplegic gait. In particular, some spatio-temporal parameters showed excellent agreement in both groups, while CoM displacements exhibited slightly lower correlation values in healthy individuals.
The results of the study suggest that a solution based on a single optical sensor could serve as an effective intermediate tool for gait analysis, not only in clinical settings or controlled environments but also in those contexts where gold standard systems are not feasible.
行走能力对于维持功能独立性至关重要，但可能会因中风等导致偏瘫的情况而受损。在中风后人群中，准确评估步态异常对于促进功能恢复以及预防跌倒或受伤的康复治疗至关重要。
本研究的目的是使用基于单个RGB-D相机（具体为Microsoft Azure Kinect DK，简称MAK）的解决方案，在健康人群（n = 27）和中风后偏瘫患者（n = 20）的短通道上评估与步态相关的参数。将该方法估计的时空参数和质心（CoM）参数与从用于仪器化3D步态分析的金标准运动捕捉（MoCap）系统获得的参数进行比较。
总体结果表明，两个系统估计的参数在健康和偏瘫步态方面均具有高水平的准确性（> 93%）和强相关性（r > 0.9）。特别是，一些时空参数在两组中均显示出极好的一致性，而质心位移在健康个体中的相关性值略低。
研究结果表明，基于单个光学传感器的解决方案不仅可以在临床环境或受控环境中，而且可以在金标准系统不可行的情况下，作为步态分析的有效中间工具。
