IOT2US lab wins 3rd place in the world-famous IPIN Competition
IoT2US Lab was awarded 3rd place for their Smartphone-based Activity Recognition and Multi-sensor Fusion based Indoor Positioning System.
Fifteen teams from top universities, institutes and high-tech companies all over the world competed in one of the most world-famous two indoor positioning competitions, IPIN and Microsoft Indoor Localization Competition (IPSN).
The solution produced is a product of IoT2US Lab based in EECS, QMUL in collaboration with UCL’s Electronic Engineering department. The team developed a Smartphone-based Activity Recognition and Multi-sensor Fusion based Indoor Positioning System
The team consisted of QMUL staff and PhD students; Bang Wu, Dr Stefan Poslad (supervisor) Xiaoshuai Zhang, Guangyuan Zhang, Zixiang Ma, along with Chengqi Ma (UCL) David Selviah (supervisor, UCL), and Wei Wu (WHU).
The goal of the competition is to evaluate the performance of different indoor localization solutions based on the signals available to a smartphone (such as WiFi readings and inertial measurements) and signals received while a person is walking through several regular unmodified multi-floor buildings. The mobility modes include ascending stairs, descending stairs, stationary, walking and stationary walking. This track is done off-site, so all data for calibration and evaluation is provided by competition organizers before the celebration of the IPIN conference.
The competition teams can calibrate their algorithmic models with several databases containing readings from sensors typically found in modern mobile phones and some ground-truth positions. Finally, each team competes using additional database files, but in this case, the ground-truth reference is not given and must be estimated by the competitors. This event is an off-line competition where all competitors have the same data of the testing environment, so custom on-site calibration is not allowed.
More information is available at http://blogger.youraisemeup920616.com/2019/10/smartphone-based-activity-recognition.html?m=1