Deep neural networks enable quantitative movement analysis using single-camera videos
推荐理由
本文提出的方法使得基于居家或于医院使用普通相机拍摄的单目视频进行人体的量化运动分析成为可能，而不再依赖于深度相机、光学动捕设备等昂贵硬件采集数据，可参考本文中提出的方法流程设计便捷的分析方法开展大规模神经肌肉类疾病患者的量化运动分析。
| 文章简介 | |
|---|---|
| 期刊 | Nature Communications |
| 发表年份 | 2020 |
| DOI | 10.1038/s41467-020-17807-z |
| 类型 | 研究性工作 |
| 领域 | 计算机视觉 |
| 引用量 | 175 |
| 推荐信息 | |
|---|---|
| 推荐人 | 阿璐思 |
| 审核 | 王伟明 张琛 |
| 推荐小组 | 动捕小组 |
摘要
Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. Here, we present a method for predicting clinically relevant motion parameters from an ordinary video of a patient. Our machine learning models predict parameters include walking speed (r=0.73), cadence (r=0.79), kneeflexion angle at maximum extension (r=0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r=0.75). These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our methods for quantifying gait pathology with commodity cameras increase access to quan- titative motion analysis in clinics and at home and enable researchers to conduct large-scale studies of neurological and musculoskeletal disorders.
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