Chapter 2. Prehension

speed, and grasping, 151
terms
abduction, 358
adduction, 358
circumduction, 358
depression, 357f, 358
elevation, 357f, 358
extension, 358
flexion, 358
opposition, 358
pronation, 357f, 358
protraction, 358
radial deviation, 357f. 358
retraction, 358
rotation, 358
supination, 357f, 358
ulnar deviation, 357f, 358
unrestrained, 191
variability, 69
Movement time
and Fins' Law, 146
and maximum aperture, 150
and object velocity, 149f
Moveslow Schema
and ballpark model, 188f
and palm-focused model of grasping, 194,
196
Mug, as-g& d object, 21,31,33f, 35.80,
82.86. g7
Muscles .
activation
commands, 135-38
computation of, 136f, 137f, 138f
by extrinsic object ropemes. 53
degrees of freedom, f14
during enclosing, 192,193f, 194
equilibrium configuration, 122
forearm EMG activity in finger opening
and pinching, 193f
level commands, and neural networks,
135-38
~~~ _.
linearization model (MLN), 128x1
pectoral girdle, 359
during preshaping, 192-94, 193f
receptors, 226,228
and stable gras 204
of upper limb, fb-64t
Myoelectric prosthetic hand, 404-5
Subject Index 473
topologies, 384-89
addtive, 389-95.399
a icial, 383,384-95
characteristics, 85.384
for choosing virtual finger mappings,
91-93,92f
connectivity pattern, 386f, 387f
for grasp selection, 85f, 85-90, 88f
and heteroassociative memory, 95.97,
395-99,396f, 398f
and joint trajectories, 131-34, 132f,
134f, 200
layers, 387-88
and mapping virtual fingers, 289
and muscle level commands, 135-38, 136f,
137f. 138f. 200
and parallel distributed processing, 383,
387-88
pattern association and com letion, 395
planning for hand location, 84-101
planning for palm orientation, 101 -5
processing, 395,400
training, 86.91-93.97, 131-32.388-94,
399-400
types. 382-83
Neural task plans, 70-76.71f. 73f, 74f
Neurally interfaced control system, for
paralyzed humans, 256
Neurmal models of rehension, 382
Nippers grasp, 2% 3%t, 372f. 375
Object prFmes
and coe icient of friction, 281
as constraints on grasping, 308t, 310-11,
325
detection of, 142-43
effect on velocity of grasping, 49-53,
5Of, 52f
extrinsic, 53,76,79-80, 143, 199,
330-3 1
and force application, 200
and grasping, 199,330-31
hardness, 231,232,233t
intrinsic, 51,53,76-79, 143.196-97,
199,330,331
and kinemahc landmarks, 155
length, 229
orientation, 53,7940,162
N
perception of, 76-80,105,231-34
Natural grasp, ma ping between object size and planning opposition space, 76-80.287
and grasp, 17%
and prehensile classificatlons, 22.25
Needle, as grasped object, 21
and sensory information, 233
Nerves, of upper limb, 366-67t
and setting up cpposition space, 146-56,
Network models of prehension, 382
293
Neural constraints, on grasping, 308t, 325
shape, 152-53.162-63, 165,231,232,
Neural events, and phases of prehension, 61
233t. 234,261-62.263.374-75
Neural networks. see ~ S Computational
Q
and stable gmp, 204,281
models of prehension
stxucmral, 231,233t
activation functions and network
surface. . 231.233t. . . 261-62
(Letters after page numbers: f=figure; n=fmtnote; t-table.)
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