Falls are significant and often fatal for vulnerable populations such as the
elderly. Previous works have addressed the detection of falls by relying on
data capture by a single sensor, images or accelerometers. In this work, we
rely on multimodal descriptors extracted from videos captured by egocentric
cameras. Our proposed method includes a late decision fusion layer that builds
on top of the extracted descriptors. Furthermore, we collect a new dataset on
which we assess our proposed approach. We believe this is the first public
dataset of its kind. The dataset comprises 10,948 video samples by 14 subjects.
We conducted ablation experiments to assess the performance of individual
feature extractors, fusion of visual information, and fusion of both visual and
audio information. Moreover, we experimented with internal and external
cross-validation. Our results demonstrate that the fusion of audio and visual
information through late decision fusion improves detection performance, making
it a promising tool for fall prevention and mitigation.

通过从自拍相机拍摄的视频中提取的多模态描述符，采用后期决策融合层建立了一种检测跌倒的方法，并通过新的数据集评估了该方法的效果，结果表明音频和视觉信息的融合通过后期决策融合可以提高检测性能，成为预防和减缓跌倒的有希望的工具。

EGOFALLS：一种使用视听数据集和基准的自我中心相机跌倒检测

EGOFALLS: A visual-audio dataset and benchmark for fall detection using  egocentric cameras

The ability to recover from an unexpected external perturbation is a
fundamental motor skill in bipedal locomotion. An effective response includes
the ability to not just recover balance and maintain stability but also to fall
in a safe manner when balance recovery is physically infeasible. For robots
associated with bipedal locomotion, such as humanoid robots and assistive
robotic devices that aid humans in walking, designing controllers which can
provide this stability and safety can prevent damage to robots or prevent
injury related medical costs. This is a challenging task because it involves
generating highly dynamic motion for a high-dimensional, non-linear and
under-actuated system with contacts. Despite prior advancements in using
model-based and optimization methods, challenges such as requirement of
extensive domain knowledge, relatively large computational time and limited
robustness to changes in dynamics still make this an open problem. In this
thesis, to address these issues we develop learning-based algorithms capable of
synthesizing push recovery control policies for two different kinds of robots :
Humanoid robots and assistive robotic devices that assist in bipedal
locomotion. Our work can be branched into two closely related directions : 1)
Learning safe falling and fall prevention strategies for humanoid robots and 2)
Learning fall prevention strategies for humans using a robotic assistive
devices. To achieve this, we introduce a set of Deep Reinforcement Learning
(DRL) algorithms to learn control policies that improve safety while using
these robots.

本论文提出了使用深度强化学习技术为双足机器人和协助机器人开发抗跌倒控制策略，涵盖了关键词：双足动物运动，机器人技术，跌倒预防，深度强化学习和控制策略。