Human Activity Recognition: Using Sensor Data of Smartphones and Smartwatches

摘要

Unobtrusive and mobile activity monitoring using ubiquitous, cheap and widely available technology is the key requirement for human activity recognition supporting novel applications, such as health monitoring. With the recent progress in wearable technology, pervasive sensing and computing has become feasible. However, recognizing complex activities on light-weight devices is a challenging task. In this work, a platform to combine off-the-shelf sensors of smartphones and smartwatches for recognizing human activities in real-time is proposed. In order to achieve the best tradeoff between the system's computational complexity and recognition accuracy, several evaluations were carried out to determine which classification algorithm and features to be used. Therefore, a data set from 16 participants was collected that includes normal daily activities and several fitness exercises. The analysis results showed that naive Bayes performs best in our experiment in both the accuracy and efficiency of classification, while the overall classification accuracy is 87% ± 2.4.