Ponce Hiram, Martínez-Villaseñor María de Lourdes, Miralles-Pechuán Luis
Faculty of Engineering, Universidad Panamericana, Mexico City 03920, Mexico.
Sensors (Basel). 2016 Jul 5;16(7):1033. doi: 10.3390/s16071033.
Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.
鉴于了解用户活动和行为有助于提供主动和个性化服务，人类活动识别在多个研究领域中受到了更多关注。有许多通过人类活动识别改进健康系统的例子。然而，人类活动识别分类过程并非易事。可穿戴传感器数据中的不同类型噪声经常妨碍人类活动识别分类过程。为了开发一个成功的活动识别系统，有必要使用能够处理噪声数据的稳定且强大的机器学习技术。在本文中，我们向人类活动识别领域介绍了人工碳氢网络（AHN）技术。我们的人工碳氢网络新方法适用于身体活动识别、对损坏数据传感器的噪声容忍以及在数据传感器的不同问题方面具有鲁棒性。我们证明了AHN分类器在身体活动识别方面非常有竞争力，并且与其他知名机器学习方法相比非常鲁棒。
