Hand Tracking, Finger Identification, and Chordic Manipulation on

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are used most often by artists <strong>and</strong> graphics designers but can also be used with h<strong>and</strong>writing recognition software. Many of these tablets support pucks as well. tap A quick press <strong>and</strong> release of the nger to the surface with minimal lateral motion. tendonitis In ammation of the tendons, the collagenous tissues which connect muscle to bone, due to overuse. tenosynivitis Swelling of the sheath which surrounds a tendon where the tendon passes over bones or curves. text cursor The cursor, usually denoted by a ashing bar or highlight block, at whichtyped characters are inserted. Can be moved incrementally by arrow <strong>and</strong> page keys. touch typing Skilled typing in which all ten ngers are used, nger motions are quick <strong>and</strong> ballistic, <strong>and</strong> the typist does not look for the keys. touchpad A credit-card-sized nger-sensing surface popular in notebook computers. Because touchpads contains long row <strong>and</strong> column electrodes rather than electrode arrays, they may detect two or three ngers but can only report a global position averaged over all nger contacts. ulnar deviation Rotated posture of the wrist in which the pinky points outward away from the sides of the body. xxviii
ABSTRACT This research introduces methods for tracking <strong>and</strong> identifying multiple nger <strong>and</strong> palm contacts as h<strong>and</strong>s approach, touch, <strong>and</strong> slide across a proximity-sensing multi-touch surface (MTS). Though MTS proximity images exhibit special topo- logical characteristics such as absence of background clutter, techniques such as bootstrapping from h<strong>and</strong>-position estimates are necessary to overcome the invisi- bility of structures linking ngertips to palms. Context-dependent segmentation of each proximity image constructs <strong>and</strong> parameterizes pixel groups corresponding to each distinguishable surface contact. Path-tracking links across successive images those groups which correspond to the same h<strong>and</strong> part, reliably detecting touchdown <strong>and</strong> lifto of individual ngers. Combinatorial optimization algorithms use biome- chanical constraints <strong>and</strong> anatomical features to associate each contact's path with a particular ngertip, thumb, or palm of either h<strong>and</strong>. Assignment of contacts to a ring of h<strong>and</strong> part attractor points using a squared-distance cost metric e ectively sorts the contact identities with respect to the ring structure. Despite the ascension of the mouse into everyday computing, more advanced devices for bimanual <strong>and</strong> high degree-of-freedom (DOF) manipulation have failed to enter the mainstream due to awkward integration with text entry devices. This work introduces a novel input integration technique which reserves synchronized motions of multiple ngers on the MTS for multi-DOF gestures <strong>and</strong> h<strong>and</strong> resting, leaving asynchronous single nger taps on the MTS to be recognized as typing on a QWERTY key layout. The operator can then switch instantaneously between typing <strong>and</strong> several 4-DOF graphical manipulation channels with a simple change in xxix
Page 1 and 2: HAND TRACKING, FINGER IDENTIFICATIO
Page 3 and 4: Signed: Signed: Signed: Signed: Sig
Page 5 and 6: Samuel Audet, for generously writin
Page 7 and 8: 1.5 What is Not Covered : : : : : :
Page 9 and 10: 3.3.2 Prediction of Contact Locatio
Page 11 and 12: 4.5.4 The Optimization Search Loop
Page 13 and 14: CONCLUSIONS : : : : : : : : : : : :
Page 15 and 16: LIST OF FIGURES 1.1 Warped QWERTY k
Page 17 and 18: 3.8 The correct segmentation for th
Page 19 and 20: 4.12 Identity swaps which occurs af
Page 21 and 22: 4.39 Palm cohesion factor versus th
Page 23 and 24: LIST OF TABLES 1.1 Legend for nger
Page 25 and 26: cubital tunnel syndrome Same as car
Page 27: path In context of Chapters 3{5, th
Page 31 and 32: Chapter 1 INTRODUCTION 1.1 The Stat
Page 33 and 34: volume of computer programming for
Page 35 and 36: esponsiveness of the controls on a
Page 37 and 38: without indentations to force the n
Page 39 and 40: Table 1.1: Legend for nger combinat
Page 41 and 42: Tapping these two ngers simultaneou
Page 43 and 44: two nger chord moves the text curso
Page 45 and 46: The proximity sensors measure the e
Page 47 and 48: TYPING RECOGNIZER FINGER SYNCHRONIZ
Page 49 and 50: hand position esti
Page 51 and 52: extract independent translation, sc
Page 53 and 54: the nger synchronization detector w
Page 55 and 56: very important preventive measures
Page 57 and 58: 1.6.2.5 Minimize user anticipation
Page 59 and 60: extracted from hand</strong
Page 61 and 62: language as they appear in communic
Page 63 and 64: 2.1.5 Sensing Finger</stron
Page 65 and 66: a) b) c) d) Figure 2.2: Projection
Page 67 and 68: a) b) c) d) Figure 2.3: Ambiguities
Page 69 and 70: few millimeters of the electrodes.
Page 71 and 72: 41 Vertical Position on Surface (Y
Page 73 and 74: 2.1.9 No Motion Blur on MTS Another
Page 75 and 76: position is indicated by the amount
Page 77 and 78: The o set-corrected image E is then
Page 79 and 80:
touches the surface and</st
Page 81 and 82:
2.3.2 Properties of H<stron
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Vertical Position on Surface (Y axi
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
PATHS FROM PREVIOUS IMAGES HAND IDE
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3.2 Contact Segmentation The object
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CURRENT PROXIMITY IMAGE DIFFUSE CUR
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proximity signal Gz, orientation G
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guessed at. Since strict segmentati
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hand position at l
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A) B) Figure 3.4: Typical search pa
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with excluded electrodes in between
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threshold Kmaxima significance. Usi
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or Si�j[n]
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etween palm heels can be measured.
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3.2.8 Extracting Group Parameters T
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For convenience while distinguishin
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Note that usually each palm heel wi
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groups. Comparing the size of the p
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any alignment within a couple centi
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a) b) Figure 3.10: Unsmoothed a) <s
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a) b) Figure 3.12: Unsmoothed a) <s
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it can get to the index ngertip <st
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444 444 11 444 1111 222 33 44 1111
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STRICT SEGMENTATION REGION 22 111 2
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STRICT SEGMENTATION REGION 111 222
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or hand resting. T
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pairing method for marginal cases d
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Ppredy[n] = Py[n ; 1] + 4tPvy[n ; 1
Page 141 and 142:
Pz[n] = Gz (3.36) P [n] = G (3.37)
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113 Vertical Position on Surface (Y
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of that contact's path by the path
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problem which must optimally match
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channels can still be reserved for
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to impose an ordering relation betw
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eliably identify all of a h
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or default hand po
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PATHS FROM PREVIOUS IMAGES HAND IDE
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when a hand is not
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4.3.1 Measuring Current H<s
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hand o set (Hmox[n
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eturns to default position. Alterna
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the estimated left hand</st
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START DEFINE IDENTITY ATTRACTORS AT
Page 171 and 172:
Vertical Surface Position (cm) 12 1
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The optimization can then be stated
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solution techniques should be e cie
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4.4.4.4 The k-exchange Sequence The
Page 179 and 180:
Pg o Pg o D D x x x x g' a Ab g' Aa
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g o h' x x a g' b g o h' x x a g' b
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This simple condition ensures that
Page 185 and 186:
4.4.5.3 Summary of Swapping Behavio
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= jj ~agjj 4 +2jj ~agjj 2 jj ~ bhjj
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fairly gradual. Though the unsquare
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constrain one another within the ar
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A1 x F1 o A7 x x A2 D F2 o A3 x o A
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inter-contact angles in the same wa
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hand contact will
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With these weighted palm heel attra
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Vertical Position on Surface (cm) 1
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experimenter then adjusts the ampli
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4.4.6.6 Palm Heel Size Factor Palm
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palm separation factor quickly decr
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where cij is the weighted distance
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are compiled. These statistics prov
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183 pinch gesture, the thumb may st
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it would erroneously grow in respon
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Like the Voronoi cells of the assig
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shifting of assignments for h<stron
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Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
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ecently lifted o nearby also proves
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surface a few dozen milliseconds af
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Vertical Surface Position (cm) 12 1
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processes can execute using the act
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can, creating an ambiguity inwhich
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213 is present or if the cluster sl
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
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(Figures 4.43, 4.44). Since thumb o
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Chapter 5 CHORDIC MANIPULATION This
Page 257 and 258:
task of moving the hand</st
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1990s with credit-card-sized touchp
Page 261 and 262:
approach could only track the nger
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graphical manipulation tasks. Such
Page 265 and 266:
e suppressed so that control occurs
Page 267 and 268:
stretchable squares, bricks, or rot
Page 269 and 270:
hand part touches
Page 271 and 272:
GET HAND'S CURRENT PATH PARAMETERS
Page 273 and 274:
ngers which lift o at about the sam
Page 275 and 276:
Note that nger pair synchronization
Page 277 and 278:
for examples of other chord tap eve
Page 279 and 280:
the path is found to be a member of
Page 281 and 282:
of the proximity pro le is measured
Page 283 and 284:
The purpose of the motion component
Page 285 and 286:
START GET HAND'S CURRENT PATH PARAM
Page 287 and 288:
the di erence between motion of a c
Page 289 and 290:
motion or control to display gain t
Page 291 and 292:
ut produces output speeds in propor
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the nger-weighted translation compo
Page 295 and 296:
Horiz. Trans. Velocity (m/s) Vert.
Page 297 and 298:
5.4.1.1 Channels Follow Fin
Page 299 and 300:
operators can set the thumb down sh
Page 301 and 302:
threshold will be crossed until nge
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Table 5.2: The simple manipulations
Page 305 and 306:
5.5 Conclusions This chapter has de
Page 307 and 308:
6.1.1 My Fitness as an Evaluator As
Page 309 and 310:
This dissertation was written in th
Page 311 and 312:
6.1.4 Weekly Symptoms 6.1.4.1 First
Page 313 and 314:
283 Figure 6.1: Modi ed Dvorak key
Page 315 and 316:
6.1.5 Recognition Errors an
Page 317 and 318:
taps or stringently adapting deboun
Page 319 and 320:
Right Hand
Page 321 and 322:
Right Hand
Page 323 and 324:
either hand, <stro
Page 325 and 326:
Table 6.5: Schedule for MTS usabili
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Subjects would continue to use thei
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during this session while subjects
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6.3.3 Universal Access People with
Page 333 and 334:
systems allow more than one pointin
Page 335 and 336:
Appendix A ERGONOMICS FOR ENGINEERS
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example, extreme wrist exion or ext
Page 339 and 340:
and an increase in
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a) b) c) d) e) f) g) Figure B.1: Di
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For medium-sized contacts as in Fig
Page 345 and 346:
Appendix C CONVERGENCE TRAPS FOR LO
Page 347 and 348:
The case of Figure C.1b contains a
Page 349 and 350:
in Figure C.2c would increase the a
Page 351 and 352:
[12] D. P. Bertsekas. A distributed
Page 353 and 354:
[37] Donald L. Fisher, Robert O. An
Page 355 and 356:
[62] Ken Hinckley, Rand</st
Page 357 and 358:
[87] Wendi Ann Latko. Development <
Page 359 and 360:
[111] Robert J. Miller, Stephen Bis
Page 361 and 362:
[136] John L. Sibert and</s
Page 363:
[161] Wayne Westerman and</
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Hand Tracking, Finger Identification, and Chordic Manipulation on

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