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We introduce AirWare, an in-air hand-gesture recognition system that uses thealready embedded speaker and microphone in most electronic devices, togetherwith embedded infrared proximity sensors. Gestures identified by AirWare areperformed in the air above a touchscreen or a mobile phone. AirWare utilizesconvolutional neural networks to classify a large vocabulary of hand gesturesusing multi-modal audio Doppler signatures and infrared (IR) sensorinformation. As opposed to other systems which use high frequency Dopplerradars or depth cameras to uniquely identify in-air gestures, AirWare does notrequire any external sensors. In our analysis, we use openly available APIs tointerface with the Samsung Galaxy S5 audio and proximity sensors for datacollection. We find that AirWare is not reliable enough for a deployableinteraction system when trying to classify a gesture set of 21 gestures, withan average true positive rate of only 50.5% per gesture. To improveperformance, we train AirWare to identify subsets of the 21 gestures vocabularybased on possible usage scenarios. We find that AirWare can identify threegesture sets with average true positive rate greater than 80% using 4--7gestures per set, which comprises a vocabulary of 16 unique in-air gestures.

AirWare: Utilizing Embedded Audio and Infrared Signals for In-Air Hand-Gesture Recognition