Margarita Grinvald. Gesture recognition for Smartphones/Wearables

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1 Margarita Grinvald Gesture recognition for Smartphones/Wearables

2 Gestures hands, face, body movements non-verbal communication human interaction 2

3 Gesture recognition interface with computers increase usability intuitive interaction 3

4 Gesture sensing Contact type: Touch based Non-contact type: Device gesture Vision based Electrical Field Sensing (EFS) 4

5 Issues on mobile devices miniaturisation lack tactile clues no link between physical and digital interactions computational power 5

6 Approaches augment environment with digital information Sixthsense [Mistry et al. SIGGRAPH 2009] Skinput [Harrison et al. CHI 2010] OmniTouch [Harrison et al. UIST 2011] 6

7 Approaches augment hardware In-air typing interface for mobile devices with vibration feedback [Niikura et al. SIGGRAPH 2010] A low-cost transparent electric field sensor for 3D interaction [Le Goc et al. CHI 2014] MagGetz [Hwang et al. UIST 2013] 7

8 Approaches efficient algorithms combine devices In-air gestures around unmodified mobile devices [Song et al. UIST 2014] Duet: Exploring Joint interactions on a smart phone and a smart watch [Chen et al. CHI 2014] 8

9 Sixthsense [Mistry et al. SIGGRAPH 2009] augment environment with visual information interact through natural hand gestures wearable to be truly mobile 9

10 Color markers Camera Projector Smartphone Mirror 10

11 Support for arbitrary surfaces 11

12 Support for multitouch 12

13 Limitations inability track surfaces differentiate hover and click accuracy limitations 13

14 Skinput [Harrison et al. CHI 2010] skin as input canvas wearable bio-acoustic sensor localisation of finger tap 14

15 Projector Armband 15

16 Mechanical phenomena finger tap on skin generates acoustic energy some energy becomes sound waves some energy transmitted through the arm 16

17 17

18 Transverse waves 18

19 Longitudinal waves 19

20 Sensing array of tuned vibrations sensors sensitive only to motion perpendicular to skin two sensing arrays to disambiguate different armband positions. 20

21 Sensor packages Weights 21

22 Tap localisation sensor data segmented into taps ML classification of location initial training stage 22

23 23

24 Limitations lack of support of other surfaces than skin no multitouch support no touch drag movement 24

25 OmniTouch [Harrison et al. UIST 2011] appropriate on demand ad hoc surfaces depth sensing and projection wearable depth driven template matching 25

26 Depth Camera Projector 26

27 Finger tracking multitouch finger tracking on arbitrary surfaces no calibration or training resolve position and distinguish hover from click 27

28 Finger segmentation Depth map Depth map gradient 28

29 Finger segmentation Candidates Tip estimation 29

30 Click detection Finger hovering Finger clicking 30

31 On demand interfaces expand application space with graphical feedback track surface on which rendered update interface as surface moves 31

32 Interface glued to surface 32

33 33

34 In-air typing interface for mobile devices with vibration feedback [Niikura et al. SIGGRAPH 2010] vision based 3D input interface detect keystroke action in the air provide vibration feedback 34

35 vibration motor white LEDs Camera 35

36 Tracking high frame rate camera wide angle lens needs distortion correction skin colour extraction to detect fingertip estimate fingertip translation, rotation and scale 36

37 Keystroke feedback difference of the dominant frequency of the fingertips scale to detect keystroke tactile feedback is important vibration feedback is conveyed after a keystroke 37

38 Vision limitations camera is rich and flexible but with limitations minimal distance between sensor and scene sensitivity to lighting changes computational overheads high power requirements 38

39 A low-cost transparent electric field sensor for 3D interaction [Le Goc et al. CHI 2014] smartphone augmented with EFS resilient to illumination changes mapping measurements to 3D finger positions. 39

40 Drive electronics Electrode array 40

41 Recognition microchip built-in 3D positioning has low accuracy Random Decision Forests for regression on raw signal data speed and accuracy 41

42 42

43 MagGetz [Hwang et al. UIST 2013] tangible control widgets for richer tactile clues wider interaction area low cost and user configurable unpowered magnets 43

44 Magnetic fields Tangibles 44

45 Tangibles traditional physical input controls with magnets magnetic traces change on widget state change track physical movement of control widgets 45

46 Tangibles magnetism Toggle switch Slider 46

47 47

48 Limitations object damage by magnets magnetometer limitations 48

49 In-air gestures around unmodified mobile devices [Song et al. UIST 2014] extend interaction space with gesturing mobile devices RGB camera robust ML based algorithm 49

50 Gesture recognition detection of salient hand parts (fingertips) works without relying on highly discriminative depth data and rich computational resources no strong assumption about users environment reasonably robust to rotation and depth variation 50

51 Recognition algorithm real time algorithm pixel labelling with random forests techniques to reduce memory footprint of classifier 51

52 Recognition steps RGB input Segmentation Labeling 52

53 Applications division of labor works on many devices new apps enabled just by collecting new data 53

54 54

55 55

56 Duet: Exploring joint interactions on a smart phone and a smart watch [Chen et al. CHI 2014] beyond usage of single device allow individual input and output joint interactions smart phone and smart watch 56

57 Design space theory conversational duet foreground interaction background interaction 57

58 Design space 58

59

60 Design space 60

61

62 Gesture recognition ML techniques on accelerometer data handedness recognition promising accuracy 62

63 Summary wearables extend interaction space to everyday surfaces augmented hardware in general provides an intuitive interface no additional hardware is preferable but there are still computational limitations combination of devices may be redundant 63

64 References SixthSense: a wearable gestural interface [Mistry et al. SIGGRAPH 2009] Skinput: Appropriating the Body As an Input Surface [Harrison et al. CHI 2010] OmniTouch: Wearable Multitouch Interaction Everywhere [Harrison et al. UIST 2011] In-air typing interface for mobile devices with vibration feedback [Niikura et al. SIGGRAPH 2010] A Low-cost Transparent EF Sensor for 3D Interaction on Mobile Devices [Le Goc et al. CHI 2014] MagGetz: customizable passive tangible controllers on and around [Hwang et al. UIST 2013] In-air gestures around unmodified mobile devices mobile devices [Song et al. UIST 2014] Duet: Exploring Joint Interactions on a Smart Phone and a Smart Watch [Chen et al. CHI 2014] 64

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