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Article

Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

1
Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000 Ljubljana, Slovenia
2
Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Iosa
Sensors 2021, 21(10), 3527; https://doi.org/10.3390/s21103527
Received: 15 April 2021 / Revised: 7 May 2021 / Accepted: 14 May 2021 / Published: 19 May 2021
(This article belongs to the Special Issue Women in Sensors)
Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements. View Full-Text
Keywords: gait model; inertial sensors; open-source dataset; smartphone; step length estimation model gait model; inertial sensors; open-source dataset; smartphone; step length estimation model
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MDPI and ACS Style

Vezočnik, M.; Kamnik, R.; Juric, M.B. Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis. Sensors 2021, 21, 3527. https://doi.org/10.3390/s21103527

AMA Style

Vezočnik M, Kamnik R, Juric MB. Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis. Sensors. 2021; 21(10):3527. https://doi.org/10.3390/s21103527

Chicago/Turabian Style

Vezočnik, Melanija, Roman Kamnik, and Matjaz B. Juric 2021. "Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis" Sensors 21, no. 10: 3527. https://doi.org/10.3390/s21103527

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