Indoor Positioning Using Shoe-Mounted Sensors

By Subhojyoti Bose and Amit Kumar Gupta

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Can we play Pokémon Go when we are inside a room or do we have to go outside every time? Why we always have to go outdoors to play Pokémon Go? The answer is simple the positioning technique used in our Smartphone, known as Global Positioning System GPS or Global Navigation Satellite System GNSS, does not work properly inside home or in the areas covered by trees or surrounded by concrete buildings. As GPS uses satellite signals for positioning, it works well under a clear sky. We spend nearly 70% of our time indoors. Therefore, there are many applications which require indoor navigation of humans and machines. But can we locate our self in a map or make use of Smartphone’s navigation utility under such circumstances? Have you ever wondered what it would have been great if GPS technology could have been used inside a big airport or large shopping mall? You often struggle to find your gate number in a big International airport. You spend a lot of time on finding the right store you want to visit in a big shopping mall. How can you beat the long search for your destination indoors? Indoor Positioning and navigation have become very important to the people in the last two decades due to setting up of huge shopping malls, airports etc where a person can easily get lost. Advancement in different technologies has paved the way for easy & accurate Indoor Positioning Systems (IPS) to tackle indoor navigation problem head-on.

Just as in-vehicle navigation systems have already revolutionized finding street addresses, IPS aim to revolutionize finding in-door/ GPS denied locations. IPS use different technologies, including distance measurement to nearby anchor nodes (nodes with known positions, e.g. WiFi access points or Bluetooth beacons), magnetic positioning, and pedestrian dead reckoning (PDR) using inertial sensors. They either actively locate mobile devices and tags, provide an ambient location of a pedestrian or environmental context for devices to get sensed.

The advancements in MEMS Technology have paved the way for low-cost inertial sensors which can be integrated into an Inertial Navigation System (INS). These MEMS-based sensors are less power hungry and small in size. This has lead to surge in using MEMS-based INS. Nowadays MEMS-based INS are often adapted in Smartphones, Smartwatches and trackers.

Shoe-mounted sensors for indoor navigation and positioning
Fig. 1: Shoe-mounted sensors for indoor navigation and positioning (Courtesy: ieee.org, reproduced with author’s permission)

Why INS?

As it was mentioned above that there are many technologies for indoor positioning. So what is the need for inertial navigation sensors for indoor positioning? Unlike other popular indoor navigation devices, INS are infrastructure free. There is no need to have infrastructure preinstall for its operation. You can use an INS practically anywhere in the world. This significantly reduces setup time. Few other important applications of infrastructure free INS are in rescue / military operations, urban mapping and surveying, gaming & VR, IoT, a variety of indoor Location Based Services (LBS) and autonomous robotics etc.

Few applications possible by using an INS
Fig.2: Few applications possible by using an INS (Courtesy: inertialelements.com)

Operating Principle

The INS apart from providing the possibility to integrate with GPS, in fact, provides a suitable alternative to the same. INS are self-contained systems that can operate in harsh radio environments. Conventional INS calculates the change in position through Dead Reckoning method. In Pedestrian Navigation this means measuring the length of the steps and the direction of the pedestrian given the initial position and heading is known. This method is often known as PDR. MEMS-based inertial sensors which form the core of any INS contains 3-axis accelerometer and 3 axis gyroscope. Now accelerometer measures acceleration and gyroscopes measure angular velocity. Displacement is calculated from the acceleration measurements and attitude is calculated from the measured angular velocity as shown in the illustration below. As the low-cost MEMS-based sensors measurements are noisy and therefore suffer from a huge accumulation of position and heading errors with time. This is because the new estimates of position and heading rely on previous estimates of the same. The error can be greatly reduced by Extended Kalman Filtering (EKF) based ZUPT approach.

Mathematical formulation of positioning from accelerometer and gyroscope data
Fig 3: Mathematical formulation of positioning from accelerometer and gyroscope data

 

It is to be noted that during a human gait there is always a momentary standstill or zero velocity moment as soon as the sole of the shoe touches the ground as can be seen in the Fig. 4.

Occurrence of standstill moment in a human step
Fig 4: Occurrence of standstill moment in a human step (Courtesy: inertialelements.com)
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