Human activity recognition is a core technology for applications such as rehabilitation, ambient health monitoring, and human-computer interactions. Wearable devices, particularly IMU sensors, can help us collect rich features of human movements that can be leveraged in activity recognition. Developing a robust classifier for activity recognition has always been of interest to researchers. One major problem is that there is usually a deficit of training data for some activities, making it difficult and sometimes impossible to develop a classifier. In this work, a novel GAN network called TheraGAN was developed to generate realistic IMU signals associated with a particular activity. The generated signal is of a 6-channel IMU. i.e., angular velocities and linear accelerations. Also, by introducing simple activities, which are meaningful subparts of a complex full-length activity, the generation process was facilitated for any activity with arbitrary length. To evaluate the generated signals, besides perceptual similarity metrics, they were applied along with real data to improve the accuracy of classifiers. The results show that the maximum increase in the f1-score belongs to the LSTM classifier by a 13.27% rise when generated data were added. This shows the validity of the generated data as well as TheraGAN as a tool to build more robust classifiers in case of imbalanced data problem.
翻译:人类活动认识是诸如康复、环境健康监测和人体计算机互动等应用的核心技术。可穿式装置,特别是IMU传感器,可以帮助我们收集在活动识别中可以利用的人类运动的丰富特征。为活动识别开发一个强大的分类器始终是研究人员感兴趣的。一个主要问题是,某些活动通常缺乏培训数据,因此很难、有时甚至不可能开发一个分类器。在这项工作中,开发了一个名为TheraGAN的新颖GAN网络,以产生与特定活动相关的现实的IMU信号。生成的信号是6频道IMU的信号,即角速度和线性加速。此外,通过引入一个简单的活动,这是复杂的全程活动的有意义的分部分。一个主要问题是,为任何任意长度的活动提供了生成过程的便利。除了视觉相似度度度量度衡量器之外,它们还被与真实数据一起应用来提高分类器的准确性。结果显示,F1芯标记的最大增加量是LSTM分类的信号,即角速度和线性加速度加速度。此外,通过一个13.27 %的数据在生成数据时生成的精确度上,数据将数据作为数据生成的精确度的精确度作为例的立值,从而显示数据生成的精确度将数据作为数据生成的精确度,从而显示数据生成的精确度增加。