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Using smartphones to track indoor movement

Using smartphones to track indoor movement

The way people walk while carrying their smart devices could help to improve the mapping of indoor areas poorly served by GPS.

As global positioning systems (GPS) become more advanced, they still face a challenge. Though they function well outdoors, location-based services (LBS) still struggle to map indoor locations reliably. A paper published in the open-access journal Mobile Information Systems entitled Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position tackles this problem by investigating how we carry our smartphones and how this affects our gait. 

The authors, including Ying Guo and Qinghua Liu from Shandong University of Science and Technology, China, used these factors to find a way to improve algorithms used in pedestrian dead reckoning (PDR). PDR is an internal phone navigation system and currently one of the primary methods used for mapping locations that obscure satellite signal transmission. 

PDR operates by estimating heading, position and crucially, the user’s gait. When we walk we may carry our phones in a variety of different positions, holding them in our hands, storing them in backpacks or pockets, and each of these positions produce different amounts of ‘jittering’, small movements which can act as noise and affect the accuracy of gait calculations. Thus, identifying various carrying positions and modeling the gait of the user in each position could be of massive benefit to PDR algorithms, improving indoor navigation and thus LBS systems as a whole.

To do this, the researchers gathered data from mobile phone accelerometer and gyroscope signals and compared them with a newly developed step estimation model based on real-time stride frequency that more accurately calculates the user’s gait. 

This allowed the team to examine various phone carrying positions and the effect of switching these positions on gait. They found that when algorithms detect and recognize phone carrying positions, the process of gait detection, and particularly aspects such as step detection and distance estimation accuracy, was greatly improved. 

Applying this to PDR technology in general, the team believes that the model they developed to identify carrying position and adjust gait accordingly could be useful in refining and improving indoor positioning, benefiting various devices that rely on LBS.

Article Details

Ying Guo, Qinghua Liu,Xianlei Ji, Shengli Wang, Mingyang Feng, and Yuxi Sun, ‘Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position,’ Mobile Information Systems, Volume 2019, Article ID 4709501, https://doi.org/10.1155/2019/4709501

This blog post is distributed under the Creative Commons Attribution License (CC-BY). The illustration is by Hindawi and is also CC-BY.

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