Challenges of Trajectory Estimation Using Inertial Sensors in Smartwatches

Jae Hong Lee, Sohee Park, Junu Park, Jae Wook Park, and Chan Gook Park

Peer Reviewed

Abstract:	This paper presents a comprehensive framework for estimating running and walking trajectories using inertial sensors in smartwatches, integrating and expanding upon our previous research. Due to the limitations of GNSS accuracy in smartwatches, especially in environments like urban canyons, we propose a smartwatch-based pedestrian dead reckoning (PDR) approach to address these challenges. Our framework combines key components: step detection, which dynamically selects the appropriate sensor signal (e.g., angular velocity or acceleration) based on the user’s activity; movement direction estimation through a kinematic model and principal component analysis (PCA) to correct arm swing misalignment; and stride length estimation using a BiLSTM/CNN-based deep learning model with a data augmentation strategy. Experimental results from walking and running exercises demonstrate that the proposed smartwatch-based method significantly improves trajectory estimation accuracy compared to GNSS, particularly in urban environments. Keywords—pedestrian dead reckoning, step detection, movement direction, step length
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	1507 - 1513
Cite this article:	Lee, Jae Hong, Park, Sohee, Park, Junu, Park, Jae Wook, Park, Chan Gook, "Challenges of Trajectory Estimation Using Inertial Sensors in Smartwatches," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 1507-1513.
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