用人体骨骼特征和MediaPipe实现室内跌倒检测，准确率更高。
Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature
- 基于MediaPipe提取人体骨骼，手工设计特征进行跌倒识别
- 在UR跌倒数据集上表现优于现有方法，跨年龄性别适用性好
- 适合养老院、居家监护等场景的低成本视觉跌倒监测
跌倒是导致致命伤害和住院的常见原因，尤其对老年人尤为重要。当前的手持设备、环境传感器和基于视觉的检测方法存在精度不足或成本过高的问题。本文提出一种基于室内视频的跌倒检测方法，利用MediaPipe框架生成人体骨骼，并提取手工特征进行分类。在UR跌倒检测数据集上的实验表明，该模型在不同年龄和性别人群的多种场景下均表现出色，分类准确率显著优于现有方法，具备良好的实用性和泛化能力。
原文摘要 · Abstract (English)
Falls are a common cause of fatal injuries and hospitalization. However, having fall detection on person, in particular for senior citizens can prove to be critical. Presently,there are handheld, ambient detector and vision-based detection techniques being utilized for fall detection. However, the approaches have issues with accuracy and cost. In this regard, in this research, an approach is proposed to detect falls in indoor environments utilizing the handcrafted features extracted from human body skeleton. The human body skeleton is formed using MediaPipe framework. Results on UR Fall detection show the superiority of our model, capable of detecting falls correctly in a wide number of settings involving people belonging to different ages and genders. This proposed model using MediaPipe for fall classification in daily activities achieves significant accuracy compare to the present existing approaches.
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