Abstract
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.
Highlights
The interest in human activity recognition research has been growing in context-aware systems for different domain applications
We propose to build and train an artificial hydrocarbon network for a supervised learning classifier (AHN classifier) aiming to monitor human activities based on wearable sensor technologies
Three experiments were conducted in order to evaluate the performance in both monitoring and noise tolerance tasks using an artificial hydrocarbon networks-based classifier in the context of the case study previously presented
Summary
The interest in human activity recognition research has been growing in context-aware systems for different domain applications. Human activity recognition (HAR) deals with the integration of sensing and reasoning in order to better understand people’s actions. Research related to human activity recognition has become relevant in pervasive and mobile computing, surveillance-based security, context-aware computing, health and ambient assistive living. Recognizing body postures and movements is especially important to support and improve health systems, as discussed below. In their survey, Avci et al [1] reviewed several medical applications of activity recognition for healthcare, wellbeing and sports systems. Regarding medical applications using HAR with wearable sensors, the authors report examples in the literature of healthcare monitoring and diagnosis systems; rehabilitation; systems to find correlation between movement and emotions; child and elderly care
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