2016 · 13 citations · 9 references
ComputingMechanical EngineeringAcousticsAssistive TechnologyUltrasoundAcoustic SensorsGesture RecognitionGesture-based InteractionMobile Human-computer InteractionMultimodal InteractionUltrasonicsAssistive Smart DevicesGesture-based InterfacesHand TrackingHand Tracking SensorsPosition EstimationSensor-based Gesture RecognitionSmart Assistive DevicesUltrasonic Sensing
A versatile and intuitive way of interacting with smart devices consists of a 3D-touchscreen in the air above the device, combined with gesture recognition. In this paper, the limitations of hand tracking and gesture recognition using existing hardware in smartphones will be investigated. To accomplish hand tracking and gesture recognition, the two loudspeakers of the device send ultrasonic tones in combination with a Maximum LengthSequence(MLS), after which the microphones pick up the potentially altered reflected signal again. The Doppler-response on the tones yields information about the speed of the hand above the device and the acoustic channel estimation obtained from the MLS estimates the distance at which the hand is present. The position estimation module estimates the 3D-position of the hand in a predefined grid of eight regions and hereby achieves an accuracy above 77 % in all cases. For the gesture recognition, features are extracted from both the Doppler-response as well as the acoustic channel estimation. A simple decision tree then classifies the performed gesture. Experimental results show that the accuracy of the gesture recognition module achieves an accuracy between 87 and 100 %.
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Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan et al. · 2016 · 399 citations
Sidhant Gupta, Daniel Morris, Shwetak Patel et al. · 2012 · 350 citations
Engineering, Physics, Acoustics +16
Bringing gesture recognition to all devices
Bryce Kellogg, Vamsi Talla, Shyamnath Gollakota · 2014 · 250 citations