Compressed sensing method for human activity sensing using mobile phone accelerometers

This paper presents the first complete design to apply the compressed sensing (CS) theory to activity sensor data gathering for smart phones. Today, most of the mobile phones are equipped with multiple sensors, such as cameras, GPS, and accelerometers. By exploiting the sensing features, we capture many different events and share them over the mobile network. One of the most important challenges for such a participatory sensing system is to reduce the battery consumption of the mobile device. We overcome this challenge by reducing the communication data, without introducing intensive computation at mobile terminals. The CS technique consists of very simple matrix operations at the mobile side, and CPU-intensive reconstruction is performed on the resource-rich machine on the network side. Since CS is a lossy compression technique, the reconstructed signal contains errors depending on the degree of sparseness of the original signal. We evaluated the proposed method by using a large amount of real activity data consisting of six basic activities performed by 90 test subjects. We also implemented our method on the iPhone/iPod platform and showed that our method can reduce power consumption by approximately 16% as compared with ZIP compression, while maintaining the error below 10%.