A Signal Processing Perspective on Human Gait: Decoupling Walking Oscillations and Gestures
A Signal Processing Perspective on Human Gait: Decoupling Walking Oscillations and Gestures
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DOI：
10.1007/978-3-030-26118-4_8
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Adrien Gregorj;Zeynep Yücel;Sunao Hara;Akito Monden;M. Shiomi
中科院分区：
文献类型：
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作者：
Adrien Gregorj;Zeynep Yücel;Sunao Hara;Akito Monden;M. Shiomi
本研究的重点是手势识别在移动的互动设置，即当互动的合作伙伴正在步行。这种互动需要特别的协调，例如，通过停留在合作伙伴的视野中，避免障碍物而不破坏群体组成，并在运动期间保持联合注意力。在文献中，各种研究已经证明，手势是在实现这些目标的密切相关。因此，一个移动的机器人在一个群体中移动与人类行人，必须识别这些手势，以维持群体协调。然而，解耦的固有-步行-振荡和手势，是一个很大的挑战，机器人。为此，我们采用在不受控制的设置记录的视频数据，并通过采用信号处理方法检测由人-人行人对执行的手臂手势。也就是说，我们利用的事实是，有一个固有的振荡运动在上肢所产生的步态，独立的视角或距离的用户到相机。我们确定手臂的姿态，这些振荡的干扰。在此过程中，我们使用一个简单的音高检测方法从语音处理，并假设涉及低频周期性的数据是免费的手势。在测试中，我们采用了一个视频数据集记录在不受控制的设置和显示，我们实现了0.80的检测率。
This study focuses on gesture recognition in mobile interaction settings, i.e. when the interacting partners are walking. This kind of interaction requires a particular coordination, e.g. by staying in the field of view of the partner, avoiding obstacles without disrupting group composition and sustaining joint attention during motion. In literature, various studies have proven that gestures are in close relation in achieving such goals.Thus, a mobile robot moving in a group with human pedestrians, has to identify such gestures to sustain group coordination. However, decoupling of the inherent -walking- oscillations and gestures, is a big challenge for the robot. To that end, we employ video data recorded in uncontrolled settings and detect arm gestures performed by human-human pedestrian pairs by adopting a signal processing approach. Namely, we exploit the fact that there is an inherent oscillatory motion at the upper limbs arising from the gait, independent of the view angle or distance of the user to the camera. We identify arm gestures as disturbances on these oscillations. In doing that, we use a simple pitch detection method from speech processing and assume data involving a low frequency periodicity to be free of gestures. In testing, we employ a video data set recorded in uncontrolled settings and show that we achieve a detection rate of 0.80.