Delivering accurate indoor location information is the holy grail for developers of positioning systems. Many existing systems rely on expensive fixed infrastructure or multiple radio signals which are often unreliable. We decided to tackle the problem a different way; using low-power, low-cost inertial sensors and algorithms that interpret the motion of the user to trace their path through a building.
The crucial factor was making the best use of the available data through the design and implementation of smart algorithms. The sophisticated mathematics in these algorithms compensates for the increased noise in smaller and cheaper sensors. These algorithms:
- Harness our understanding of inertial sensors and the mechanics of human motion gained from extensive work in fitness technology development
- Perform data fusion using Bayesian statistics to incorporate any additional information available to improve performance. For example, if a building map is available, this can be used to infer a user’s stride length by counting how many steps it takes to walk down a corridor of known length
- Allow us to achieve the same performance that previously required expensive bulky equipment in a prototype that is small enough to be clipped on to a belt
The resulting Trace positioning system is able to provide indoor location estimates even in the absence of external references, such as GPS or radio signals, which are needed for existing systems. Accuracy to within a few per cent of the distance travelled, with no external references, has been achieved in a range of trials.
Potential applications of the technology include locating firefighters inside smoke-filled buildings or pinpointing the nearest doctor in a large hospital during an emergency. For consumers, it opens up the possibility of using map apps to find someone’s location – even when GPS is unavailable. It could enable the tracking of athletes, pets, children or the elderly, for example.