Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distillation for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect.

使用各种传感器进行人体动作识别是自动驾驶汽车，人形机器人和周围环境的必不可少的组成部分。特别感兴趣的是跌倒的检测和识别。在本文中，我们建议使用由知识蒸馏引导的时间卷积网络来检测跌倒并使用加速度计数据识别跌倒的类型。当发生动作时，安装在人体上的三轴加速度计可测量人体关节的加速度。这些数据用于模式分析和身体动作识别。我们展示了识别人体动作时由软生物识别技术引起的偏见的存在。我们引入因果网络以捕获偏差对系统性能的影响，并说明如何应用知识蒸馏来减轻偏差影响。