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Indoor Localization and Tracking Systems
Published in Yufeng Wang, Athanasios V. Vasilakos, Qun Jin, Hongbo Zhu, Device-to-Device based Proximity Service, 2017
Yufeng Wang, Athanasios V. Vasilakos, Qun Jin, Hongbo Zhu
Step detection is a basic module in most inertial based pedestrian localization and navigation systems. The physical underpinning is to search for cycles in acceleration traces to capture the repetitive movements during walking. When an acceleration trace was given as an input, step detection algorithms slice and label the trace into steps exploiting the repetitive patterns of walking, and the labels are then summed into step counts. Roughly these algorithms can be grouped as follows:
Pedestrian positioning using smartphones in building with atypical geometry
Published in Soňa Molčíková, Viera Hurčíková, Vladislava Zelizňaková, Peter Blišťan, Advances and Trends in Geodesy, Cartography and Geoinformatics, 2018
E. Erdelyiova, P. Kajanek, A. Kopacik
To eliminate the systematic errors of the accelerometer, the proposed model uses a step detection method complemented by an adaptive step length algorithm which defines step length based on walking frequency and average amplitude of acceleration.
A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals
Published in Journal of Medical Engineering & Technology, 2018
Matthew B. Rhudy, Joseph M. Mahoney
Before applying any step detection techniques, the three axes of the accelerometer and gyroscope data were zero-lag filtered using a fourth-order low-pass Butterworth filter with cut-off frequency of 4 Hz. Low-pass filters are commonly used in step detection algorithms, e.g. [16,23], to reduce undesirable sensor noise in the data. After filtering, the magnitude of acceleration is calculated from the three axes of accelerometer data, and the magnitude of the angular velocity is calculated from the three axes of gyroscope data. Using acceleration and angular velocity magnitude removes the dependence on the orientation of the sensor, thus making it more generally applicable.
Validity of the “Samsung Health” application to measure steps: A study with two different samsung smartphones
Published in Journal of Sports Sciences, 2019
Vicente J. Beltrán-Carrillo, Alejandro Jiménez-Loaisa, Miriam Alarcón-López, Jose L. L. Elvira
The validity of Samsung Health also varied depending on the body location of the smartphone (waist, arm and hand) and the type of gait (walking and running) (see Table 2). In contrast, the only previous study with the Samsung Health application (Johnson et al., 2016) found no significant differences between Samsung Health estimated steps and the measures of the pedometer StepWatch 3 while walking or running, regardless of the body location of the smartphone (pants pocket and hand). These results are understandable considering the methodological differences between the study of Johnson et al. (2016) and our study (see last paragraph of the introduction). Concretely, the differences could be due to the fact that Johnson et al. (2016) used an indirect and less accurate criterion measure of step counts. Another possible explanation is that the front pants pocket could be as valid a body location as the hand, although our study did not analyse this specific issue because the pants pocket location was not included. Nevertheless, the findings of our study are congruent with other previous research. Åkerberg et al. (2012) found more valid step counts with the smartphone located in the front pants pocket than in the chest pocket of the jacket or on the arm. Leong and Wong (2016), when testing the validity of three pedometer applications in laboratory, confirmed that the pedometer application improved in step counting validity when the walking speed on a treadmill increased and the smartphone was placed in the front pants pocket (compared to the waist and the arm). This increase in validity as walking speed increases was also found in studies with electronic pedometers (Crouter, Schneider, Karabulut, & Bassett, 2003; Dondzila et al., 2012). It seems that faster movements generate higher accelerations that can trigger step detection by different smartphone applications (Bergman et al., 2012; Leong & Wong, 2016).