Smartphone is broadly applicable to the human activity recognition HAR mobile devices. However, energy consumption becomes a big obstacle to such mobile devices of real-time monitoring. In order to solve this problem, this paper presents a method of activity recognition based on energy-efficient schemes. In terms of data acquisition and processing, energy-efficient schemes adopt the best sample rate and extract the most effective feature combinations in accordance with the different activities, so as not to increase energy consumption; while in terms of recognition algorithm, we adopt the improved structure of multi-class support vector machine, combine it with the probability of activity occurrence, so as to reduce the time complexity of recognition. This method can minimize energy consumption greatly under the premise of maintaining higher recognition accuracy. Moreover, this paper adopts mobile cloud security technology to reduce potential risk of the smartphone's data transmission and processing. We present our work with an experimental study, and our experiments show that the accuracy of activity recognition based on energy-efficient schemes we proposed is up to 90.6%. In addition, this method will save 51.0% energy than that when sample rate and extracted features are, respectively, fixed at 100Hz and combined features. Copyright © 2016 John Wiley & Sons, Ltd.
['Zhide Chen', 'Jiyun Wu', 'Aniello Castiglione', 'Wei Wu']
Human continuous activity recognition based on energy-efficient schemes considering cloud security technology