11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Gravity and Linear Acceleration Estimation on Mobile Devices

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  • @INPROCEEDINGS{10.4108/icst.mobiquitous.2014.258034,
        author={Samuli Hemminki and Petteri Nurmi and Sasu Tarkoma},
        title={Gravity and Linear Acceleration Estimation on Mobile Devices},
        proceedings={11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ICST},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={11},
        keywords={mobile sensing linear acceleration gravity estimation},
        doi={10.4108/icst.mobiquitous.2014.258034}
    }
    
  • Samuli Hemminki
    Petteri Nurmi
    Sasu Tarkoma
    Year: 2014
    Gravity and Linear Acceleration Estimation on Mobile Devices
    MOBIQUITOUS
    ICST
    DOI: 10.4108/icst.mobiquitous.2014.258034
Samuli Hemminki1,*, Petteri Nurmi1, Sasu Tarkoma1
  • 1: Helsinki Institute for Information Technology HIIT
*Contact email: shemmink@cs.helsinki.fi

Abstract

Linear acceleration is an important enabler for many applications of mobile and wearable activity recognition. The most common approach for estimating linear acceleration is to estimate the gravity component of accelerometer measurements and to project gravity-eliminated accelerometer measurements onto horizontal and vertical planes. Consequently, the accuracy of the linear acceleration estimates is highly dependent on the accuracy and robustness of the underlying gravity estimation algorithm. The present paper contributes by developing a novel approach for gravity and linear acceleration estimation from accelerometer and gyroscope measurements. Our approach improves on previous solutions by (i) providing increased robustness in the presence of sustained acceleration; (ii) detecting and filtering out common types of noise, such as centripetal forces and shifts in device orientation caused by spontaneous user interactions; (iii) operating on shorter time windows, making our approach suitable for applications that require rapid updates on user activities; and by (iv) distinguishing between lateral and longitudinal components of linear acceleration. Experiments carried out using over 100 hours of measurements demonstrate that our approach results in significant improvements in the accuracy of linear acceleration estimates, and improves robustness against common sources of noise in the estimation process. Specifically, our method achieves over 40% improvements in the accuracy of reconstructing speed information and over 70% improvements in the accuracy of estimating travel distances.