Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study

被引:60
作者
Moro, Matteo [1 ,2 ,3 ]
Marchesi, Giorgia [1 ,3 ]
Hesse, Filip [1 ]
Odone, Francesca [1 ,2 ]
Casadio, Maura [1 ,3 ]
机构
[1] Univ Genoa, Dept Informat Bioengn Robot & Syst Engn DIBRIS, I-16145 Genoa, Italy
[2] Machine Learning Genoa MaLGa Ctr, I-16146 Genoa, Italy
[3] Spinal Cord Italian Lab SCL, I-17027 Pietra Ligure, Italy
关键词
markerless; human motion analysis; gait analysis; computer vision; deep learning; MOTION CAPTURE SYSTEM; TRACKING; DATASET;
D O I
10.3390/s22052011
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.
引用
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页数:15
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