Motionword: An activity recognition algorithm based on intelligent terminal and cloud

Abstract

The ability to recognize physical activity, such as sedentary, driving, riding, daily activities and effective training, is useful for health conscious users to catalogue their daily activities and to develop good exercise routines. Conventional activity recognition algorithms require complex calculations, which are not suitable for wearable devices developed on low-cost, low-power hardware platforms. In this paper, inspired by the text mining related work, we design a novel activity recognition algorithm, which is named “Motionword”. In the wearable device proper, a lightweight recognition algorithm is adopted to compute in real-time predefined atomic events, and count the frequency that these events occur, resulting in a data summary, and then the data summary is transmitted to the platform. On the platform, intelligent method is used to identify and categorize the user's main activity into 5 classes. The test results on a dataset composed of 110 user∗day real world data, contributed by 10 users, show that the recognition accuracy is 95.52%. The Motionword algorithm is capable of achieving accurate activity recognition results without additional hardware cost or power consumption.