Earlier this month, I was invited to deliver the closing keynote at the Wearable Technology Show in London and I chose to speak about trusted technology. This is a theme that has come to define my career and was the main reason why I started Focal Point Positioning three years ago; to bring reliability, integrity and high accuracies to a sector that desperately needs it.

Trust is particularly interesting in the case of wearable technologies because it must flow in both directions. We must be able to trust our devices, to know confidently what information they can generate about us and for us, but it is also crucial that our wearables and smartphones can trust us to use them correctly, in order to function as desired. Let me explain.

Let’s look at Pokemon Go; a very famous location-based game where players must collect monsters from specific geographical locations. Users can also hatch eggs containing monsters by walking large distances of many kilometres. The length that gamers will go to in order to cheat at these kind of games is impressive – especially, it would appear, where exercise is concerned! A variety of cheating methods for egg-hatching have been posted on forums and YouTube, such as placing phones on turntable record players, ceiling fans, or attaching them to bicycle wheels. The most extreme (and illegal) that I came across involved using a GPS simulator and a software defined radio to spoof the GPS receiver into believing that the user was really travelling to the locations of rare Pokemon.

In both of these cases it is possible to write smarter code within Pokemon Go or on the smartphone O/S that can detect and ignore the lies. In the case of tricking step detectors, realistic waveform patterns of true pedestrian motion can be captured across the full set of sensors (accelerometer, gyroscope and magnetometer) and cross checked against those being generated live. This is known as context detection, and is used to classify the placements of wearable devices (bag, hand, wrist, arm, hip, etc) and the type of motion (walking, running, cycling, vehicle, etc). The waveform patterns for all the cheating methods mentioned above would not be classified as actual walking motion, and would be ignored. In the case of GPS spoofing, it is in general easy to spoof one type of positioning system, but much more challenging to spoof multiple systems such that they agree with each other. For example. a smartphone contains WiFi, BLE, cellular, and inertial sensors (step detectors and motion-context recognisers). Cross-checking these sensors with the spoofed GPS receiver can easily reveal that large-scale motions according to the GPS receiver simply don’t stack up with the other measurements. So, while our devices sometimes cannot trust their end users, smarter software can still be written to beat the cheaters.

A much more serious example of step-detection-spoofing comes in the form of cheating on your insurance premiums. There are many examples, especially in the USA, of insurance companies offering discounts of up to $1,500 and rewards vouchers totalling hundreds of dollars for people exercising regularly, as monitored by fitness trackers. Fitness trackers that can today be cheated…

Enter Focal Point’s D-Tail and S-GNSS solutions, which both fuse together data from a variety of sources, and provide a much more robust and reliable way of determining motion through space. Our D-Tail technology is very well suited to sports and fitness applications and is going to be deployed on a variety of wearable devices. For sportsmen and women desperate to beat their real personal best when they go for a run, overdue software updates like these could make a huge difference to their performance – but perhaps even more important is the fact that in other sectors it will force consumers, and their wearables, to tell the truth.