What components went into developing Peer to Peer Contextual Awareness Sharing system and why?
A In the Hackathon we used Bosch BNO555 sensor for measuring the inertial measurement as this was the primary focus of the Hackathon, to develop apps around it. A custom signal filtering algorithm was developed that was the key to achieve 1m positioning accuracy.
The other components were the Arduino prototyping boards that were used to get the data from the sensor and push it over to a laptop via Bluetooth module. These modules being easy to work with help DIY-ers get a system up easily and quickly, helping to focus on application building rather than focusing on nuts and bolts of getting the device driver, OS level minutiae.
What are the operational benefits of having such a system?
A In a context where indoor localization is required, most of the existing solutions that are Wi-Fi based or other beacon based are not accurate to level of +/- 1 m accuracy and expensive. The usage of highly tuned inertial measurement unit as an app or wearable, enables positioning to a greater degree of accuracy without additional infrastructural intervention.
Use cases like indoor localization (where GPS is not usable) in retail, logistics, event management and building security can highly benefit from contextual insights generated by such a positional accuracy and sharing of the same with peers in real time.
Could you brief about your application interface?
A At a user interface level – a screen that is displayed on a PC showing movements of tracked entities, the path the entities have taken.
At the software level – an API (Application Program Interface) disseminates the positional parameters (distance, speed, acceleration and orientation) to the authorized subscribers. With the API it is possible to mirror the positional and path data on multiple clients, on a smartphone and a website for example.
How do you analyse heat map, Head tracking etc.?
A The prototype in the Hackathon didn’t involve head tracking, but the idea was to take it to Google glass like device in the next step. This gives the possibility of even capturing eyeballs for understanding advertising attractiveness for example.
A web based application generates the heat map based on traffic of the movements and dwell times. The heat map intensity is proportional to footfall, for example use case in retail.
How does the system benefit the whole value chain? What are the challenges?
A The final version is expected to be a wearable or a smartphone app. For the end user it provides the convenience of location and contextual sharing easily.
For the establishments where deployment is done, (the place where this system is deployed, in a warehouse or retail for example), personnel level workflow tracking and heat map provides insights into multiple metrics like absolute footfall, footfall-conversion ratio, average dwell times, derived brand attractiveness etc.
Click here to read about Winners of Bosch Hackathon.