Real-time Sound Analysis to Count Opening Cycles of Automatic Doors

Abstract: Counting opening cycles on an automatic sliding door is of great interest for a company manufacturing doors, such as ASSA Abloy. These metrics could be used for consumer statistics or for door diagnostics. Counting opening cycles is seemingly trivial when there is access to the door’s internal diagnostics or having adequate sensors. Problems start to arise when these are not present, which is the case when working with third party door vendors, and sensors can often be expensive and time consuming to install. This report investigates the process of counting opening cycles using real-time sound analysis on a microcontroller. Due to microphones having low cost and lack need for precise placement, ASSA Abloy is considering implementing this on their IoT gateway hardware, ready to be mounted on any third party door. To achieve this, two different algorithms were tested - signal energy analysis and Mel Frequency Cepstrum Coefficient (MFCC) analysis. An implementation was also tested were both algorithms were used. These were then tested on four different kinds of automatic doors. Signal energy detection performs well on all types of doors but is prone to false positives from external noise. MFCC detection is more resistant to false positives from a noisy environment, but often detects false positives during the closing of the door, and only gives good results for two doors. The combined algorithm similarly only performed well for two of the doors, but had the fewest false positives. These results show that in an ideal environment signal energy detection will suffice, but in noisy scenarios the combined algorithms show promise, assuming the MFCC detection can be improved or an initial calibration to the door is added.