Abstract
Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.
Highlights
Nowadays, improvements in medicine have increased the average age of the world’s population
We propose a novel activity recognition approach based on a multi-class support vector machine (SVM) framework
In order to address the problem stated above, we propose a novel activity recognition approach based on genetic algorithm–optimized SVM
Summary
Improvements in medicine have increased the average age of the world’s population. The United Nations predicts that by 2050, 22% of the world’s population will be above 65 years of age.[1] As a result, most nations have to face the demographic modification problem and need to develop a series of healthcare technologies to help elderly people live their life in good conditions. Given a group of training samples, SVM aims to find the training cases that lie on the class boundaries, that is, the support vectors. These support vectors can determine an optimal separating hyperplane (OSH) between different classes.
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