How to run the model

The file das3.mexw64 (or das3.mexw32 for 32-bit) is a Matlab MEX function that contains the system dynamics, and other functions, accessible via a Matlab function interface. The file model_struct.mat contains model parameters for the passive joint properties, muscle models, muscle path polynomials and GH force vector polynomials. During initialization, the MEX function reads in the parameters from struct model.

To run das3driver, which displays the model movement in real time using the OpenSim Visualizer, you have to set up the Opensim-Matlab scripting environment. To do this, follow these instructions: http://simtk-confluence.stanford.edu:8080/display/OpenSim/Scripting+with+Matlab

Contents

How to use the model

The MEX function has several ways in which it can be used.

Initialization

This needs to be done first.

load model_struct;
das3('Initialize',model);
*****************************************************
*                      DAS3MEX                      *
*  (c) 2010-2012 Case Western Reserve University    *
*****************************************************
Initializing...

Dynamics

This is to evaluate the model dynamics in the implicit form f(x, xdot, u) = 0.

Inputs

Optional inputs

Outputs

Optional outputs

Notes:

  1. FGH is only correct when dynamics are satisfied, i.e. f is zero
  2. The three Jacobians must always be requested together, or not at all.
load('equilibrium.mat');
ndof = model.nDofs;
nmus = model.nMus;
nstates = 2*ndof + 2*nmus;
xdot = zeros(nstates,1);
u = zeros(nmus,1);
M = zeros(5,1);
exF = zeros(2,1);
handF = zeros(3,1);

[f, dfdx, dfdxdot, dfdu, FGH, FSCAP, qTH] = das3('Dynamics',x, xdot, u, M, exF, handF);

Extract mass of the muscle elements

Output

MusMass = das3('MuscleMass')
MusMass =

    0.0195
    0.0216
    0.0242
    0.0149
    0.0128
    0.0103
    0.0096
    0.0121
    0.0142
    0.0116
    0.0141
    0.0171
    0.0097
    0.0173
    0.0154
    0.0041
    0.0132
    0.0130
    0.0072
    0.0057
    0.0113
    0.0203
    0.0118
    0.0089
    0.0114
    0.0208
    0.0193
    0.0163
    0.0091
    0.0135
    0.0061
    0.0073
    0.0072
    0.0048
    0.0023
    0.0121
    0.0370
    0.0227
    0.0421
    0.0089
    0.0089
    0.0249
    0.0127
    0.0173
    0.0238
    0.0379
    0.0201
    0.0122
    0.0170
    0.0098
    0.0126
    0.0151
    0.0115
    0.0094
    0.0196
    0.0225
    0.0149
    0.0163
    0.0241
    0.0156
    0.0071
    0.0122
    0.0104
    0.0264
    0.0155
    0.0348
    0.0197
    0.0074
    0.0076
    0.0165
    0.0082
    0.0028
    0.0057
    0.0202
    0.0238
    0.0103
    0.0129
    0.0151
    0.0053
    0.0182
    0.0036
    0.0012
    0.0662
    0.0208
    0.0311
    0.0270
    0.0296
    0.0296
    0.0320
    0.0150
    0.0288
    0.0430
    0.0352
    0.0316
    0.0407
    0.0269
    0.0245
    0.0348
    0.0330
    0.0227
    0.0324
    0.0277
    0.0140
    0.0184
    0.0176
    0.0120
    0.0140
    0.0077
    0.0291
    0.0159
    0.0147
    0.0070
    0.0066
    0.0041
    0.0065
    0.0047
    0.0140
    0.0060
    0.0275
    0.0043
    0.0008
    0.0036
    0.0034
    0.0030
    0.0068
    0.0016
    0.0035
    0.0049
    0.0125
    0.0332
    0.0152
    0.0162
    0.0059
    0.0008
    0.0015
    0.0006
    0.0022
    0.0033

Extract LCEopt of the muscle elements

Output

LCEopt = das3('LCEopt')
LCEopt =

    0.1446
    0.1127
    0.1070
    0.0952
    0.0924
    0.0893
    0.0868
    0.0770
    0.1011
    0.1264
    0.1355
    0.1547
    0.1680
    0.1578
    0.1302
    0.0765
    0.1086
    0.1075
    0.1381
    0.0986
    0.1077
    0.1259
    0.1267
    0.1170
    0.1635
    0.1667
    0.1622
    0.1289
    0.1259
    0.1220
    0.1133
    0.0845
    0.0800
    0.0660
    0.0885
    0.0945
    0.1128
    0.0949
    0.0833
    0.0666
    0.0656
    0.0767
    0.0754
    0.0710
    0.0732
    0.0748
    0.0737
    0.0836
    0.0940
    0.0824
    0.0991
    0.0660
    0.0683
    0.0727
    0.0633
    0.0719
    0.0677
    0.0693
    0.0698
    0.0753
    0.0501
    0.0550
    0.0506
    0.1411
    0.1595
    0.1526
    0.1093
    0.0541
    0.0591
    0.0564
    0.0554
    0.0556
    0.0649
    0.0676
    0.0744
    0.0797
    0.0762
    0.0820
    0.0765
    0.0721
    0.0676
    0.0643
    0.1292
    0.1170
    0.1149
    0.1008
    0.0881
    0.1183
    0.1226
    0.2343
    0.2857
    0.2951
    0.3206
    0.3129
    0.3402
    0.1884
    0.1830
    0.1853
    0.1833
    0.1500
    0.1449
    0.1363
    0.1091
    0.0807
    0.0773
    0.0602
    0.0746
    0.0807
    0.0876
    0.0953
    0.0870
    0.0758
    0.0619
    0.0472
    0.0621
    0.1613
    0.1613
    0.1066
    0.0610
    0.0391
    0.0267
    0.0260
    0.0252
    0.0263
    0.0274
    0.0377
    0.0386
    0.0352
    0.0758
    0.0766
    0.0656
    0.0638
    0.0638
    0.0252
    0.0202
    0.0228
    0.0270
    0.0266

Extract SEEslack of the muscle elements

Output

SEEslack = das3('SEEslack')
SEEslack =

    0.0100
    0.0270
    0.0540
    0.0530
    0.0350
    0.0330
    0.0360
    0.0480
    0.0340
    0.0350
    0.0310
    0.0470
    0.0420
    0.0190
    0.0180
    0.0510
    0.0410
    0.0290
    0.0260
    0.0150
    0.0660
    0.0320
    0.0310
    0.0400
    0.0250
    0.0090
    0.0070
    0.0110
    0.0110
    0.0110
    0.0090
    0.0110
    0.0070
    0.0050
    0.0050
    0.0050
    0.0800
    0.0760
    0.0620
    0.0450
    0.0350
    0.0670
    0.0820
    0.0650
    0.0710
    0.0640
    0.0480
    0.0620
    0.0880
    0.0670
    0.0730
    0.0920
    0.1040
    0.1130
    0.0380
    0.1070
    0.0840
    0.0710
    0.0500
    0.0350
    0.0490
    0.0510
    0.0180
    0.0060
    0.0120
    0.0150
    0.0340
    0.0170
    0.0250
    0.0520
    0.0310
    0.0280
    0.0440
    0.0590
    0.0550
    0.0660
    0.0770
    0.0790
    0.0400
    0.0590
    0.0100
    0.0100
    0.2335
    0.1913
    0.1927
    0.2041
    0.2193
    0.2180
    0.2197
    0.0900
    0.0830
    0.1050
    0.1200
    0.0720
    0.0690
    0.0360
    0.0430
    0.0530
    0.0280
    0.0260
    0.0360
    0.0170
    0.0140
    0.0875
    0.1122
    0.0107
    0.0404
    0.0738
    0.0807
    0.0592
    0.0394
    0.0317
    0.0209
    0.0350
    0.0076
    0.1197
    0.1197
    0.1542
    0.0851
    0.0484
    0.0254
    0.0231
    0.0150
    0.0145
    0.0139
    0.0050
    0.0050
    0.0050
    0.1707
    0.1770
    0.1911
    0.1570
    0.1570
    0.0064
    0.0080
    0.0043
    0.0300
    0.0200

Extract dof limits

Output

limits = das3('Limits')
limits =

  Columns 1 through 7

   -1.1345    0.0873         0    0.5760   -0.3819   -0.2967   -2.9671
   -0.3316    0.5236    1.4486    1.2043    0.3491    0.3142    3.0543

  Columns 8 through 11

   -0.5236   -3.0892    0.0873    0.0873
    1.4661    3.1241    2.4435    2.7925

Compute stick figure coordinates

Inputs

Outputs

Notes:

# Output only depends on the first ndofs elements of x (the joint angles)

stick = das3('Visualization', x)
stick =

  Columns 1 through 7

         0         0         0    1.0000         0         0         0
    0.0014   -0.0152    0.0028    0.9320   -0.0000   -0.3624   -0.0000
    0.0014   -0.0152    0.0028    0.9239   -0.1225   -0.3624    0.1314
    0.0014   -0.0152    0.0028    0.9239   -0.1995   -0.3264    0.1314
    0.1841    0.0058    0.1033    0.9415   -0.1995    0.2717    0.2342
    0.1841    0.0058    0.1033    0.9378    0.2161    0.2717   -0.1953
    0.1841    0.0058    0.1033    0.9378    0.1218    0.3251   -0.1953
    0.1689   -0.0256    0.0681    0.7140    0.1218    0.6894   -0.2629
    0.1689   -0.0256    0.0681    0.6854   -0.2342    0.6894    0.2275
    0.1689   -0.0256    0.0681    0.4741   -0.2342    0.8487    0.1914
    0.2439   -0.3052    0.0404    0.4686    0.3393    0.8156    0.1798
    0.2341   -0.3061    0.0152   -0.6923    0.2308    0.6837    0.5711
    0.1218   -0.4893   -0.0840   -0.6923    0.2308    0.6837    0.5711

  Columns 8 through 12

    1.0000         0         0         0    1.0000
    1.0000   -0.0000    0.3624   -0.0000    0.9320
    0.9913   -0.0000    0.3592   -0.0476    0.9320
    0.9668   -0.2190    0.3592    0.1594    0.9195
    0.9668   -0.1017   -0.2424    0.1594    0.9570
    0.9755   -0.1017   -0.2871    0.0423    0.9570
    0.9593    0.2038   -0.2871   -0.2546    0.9235
    0.9593    0.1027   -0.6489   -0.2546    0.7170
    0.9683    0.1027   -0.6917    0.0865    0.7170
    0.9683    0.1603   -0.8594    0.0865    0.5040
    0.8673   -0.4641   -0.8649    0.3642    0.3455
    0.7544    0.3236   -0.4411    0.6145   -0.6541
    0.7544    0.3236   -0.4411    0.6145   -0.6541

Compute scapula contact

Inputs

Outputs

F_contact = das3('Scapulacontact', x)
F_contact =

   -0.0231
   -0.0132

Compute joint moments

Inputs

Outputs

moments = das3('Jointmoments', x)
moments =

    4.0430
  -12.5695
   26.3852
  -12.3026
   40.6593
   18.4577
   -0.0436
    1.3738
   -0.2188
    0.6510
    0.0400

Compute muscle forces

Inputs

Outputs

forces = das3('Muscleforces', x)
forces =

   19.1221
    7.8202
   -0.1549
   -0.1781
   -0.1266
   -0.0520
    0.0047
   12.1040
   28.9155
   16.8419
   41.1334
   -0.1646
   -0.2617
   -0.2206
   -0.2586
   -0.1274
   -0.1426
   -0.2100
   -0.2083
   -0.1906
   -0.1309
   -0.3063
   -0.4244
   -0.4801
    2.3729
   11.8943
   22.6993
   81.1908
   35.7430
   20.0015
    0.0349
    3.0283
   -0.0247
   -0.1091
   -0.1989
   -0.2885
    0.4169
   -0.0260
   -0.0086
    2.3817
    2.1835
    0.2761
    0.7456
    0.0824
   -0.0018
   -0.0488
   -0.0260
   51.1191
   -0.2471
   -0.2043
    9.1881
    0.1107
    0.1534
    0.0982
   -0.0428
   -0.0156
   -0.0045
   -0.0403
   -0.0433
   -0.0561
   -0.0374
   -0.0403
   -0.0372
   -0.1352
   -0.1311
   -0.1600
   -0.1823
    6.3710
    3.1391
   -0.0156
   -0.0157
   -0.1005
   -0.0924
   -0.1221
   -0.1132
   -0.1813
   -0.1009
   -0.1661
   -0.1640
   -0.1258
   -0.1179
   -0.1658
   -0.1974
   -0.1684
   -0.2171
   -0.1657
   -0.1262
   -0.1364
   -0.0856
   -0.6512
   -0.6985
   -0.7307
   -0.6699
   -0.5558
   -0.8528
   -0.2849
   -0.1576
   -0.1937
   -0.0905
    8.1968
   -0.0096
    5.0386
   -0.0249
   -0.1189
   -0.1188
   -0.1064
   -0.1199
   -0.0733
   -0.0421
   -0.0236
    0.0027
   -0.0284
   -0.0081
   -0.0239
   -0.0132
    0.3316
   -0.0012
    0.8262
   -0.0118
    1.7257
   -0.0410
   -0.0665
   -0.0359
   -0.0254
   -0.0315
    1.9911
    0.8073
   -0.0026
   -0.0961
   -0.1180
   -0.0997
   -0.1171
   -0.0618
   -0.0346
   -0.0162
    0.0325
   -0.0144
   -0.0294

Compute moment arms

Inputs

Outputs

momentarms = das3('Momentarms', x)
momentarms =

   (1,1)      -0.1276
   (2,1)      -0.0814
   (3,1)      -0.1223
   (4,1)      -0.1349
   (5,1)      -0.1257
   (6,1)      -0.1217
   (7,1)      -0.1466
   (8,1)      -0.1252
   (9,1)      -0.1086
  (10,1)      -0.0833
  (11,1)      -0.0554
  (12,1)      -0.0840
  (13,1)      -0.0699
  (14,1)      -0.0672
  (15,1)       0.0046
  (16,1)       0.0419
  (17,1)       0.0404
  (18,1)       0.0510
  (19,1)       0.0382
  (20,1)      -0.0340
  (21,1)      -0.0485
  (22,1)      -0.0240
  (23,1)      -0.0221
  (24,1)      -0.0035
  (25,1)       0.0745
  (26,1)       0.0611
  (27,1)       0.0869
  (28,1)       0.0844
  (29,1)       0.0863
  (30,1)       0.1231
  (31,1)       0.1003
  (32,1)       0.0714
  (33,1)       0.0828
  (34,1)       0.1166
  (35,1)       0.0804
  (36,1)       0.0888
  (90,1)      -0.0650
  (91,1)      -0.0419
  (92,1)      -0.0252
  (93,1)      -0.0324
  (94,1)      -0.0203
  (95,1)      -0.0115
  (96,1)       0.0362
  (97,1)       0.0674
  (98,1)       0.0646
  (99,1)       0.0186
 (100,1)       0.0204
 (101,1)       0.0190
   (1,2)       0.0488
   (2,2)       0.0933
   (3,2)       0.0570
   (4,2)       0.0532
   (5,2)       0.0464
   (6,2)       0.0105
   (7,2)      -0.0102
   (8,2)      -0.0311
   (9,2)      -0.0734
  (10,2)      -0.0678
  (11,2)      -0.0914
  (12,2)       0.0728
  (13,2)       0.0454
  (14,2)       0.0949
  (15,2)       0.0217
  (16,2)      -0.0287
  (17,2)      -0.0580
  (18,2)      -0.0690
  (19,2)      -0.0966
  (20,2)       0.0712
  (21,2)       0.0812
  (22,2)       0.0644
  (23,2)       0.0179
  (24,2)       0.3844
  (25,2)      -0.1040
  (26,2)      -0.3057
  (27,2)      -0.0483
  (28,2)      -0.0443
  (29,2)       0.0364
  (30,2)       0.0074
  (31,2)       0.0287
  (32,2)       0.0209
  (33,2)       0.0126
  (34,2)      -0.0014
  (35,2)      -0.0472
  (36,2)      -0.0607
  (90,2)      -0.0802
  (91,2)      -0.0621
  (92,2)      -0.0929
  (93,2)      -0.1119
  (94,2)      -0.1565
  (95,2)      -0.1117
  (96,2)      -0.1049
  (97,2)      -0.0980
  (98,2)      -0.0602
  (99,2)      -0.1842
 (100,2)      -0.0238
 (101,2)      -0.0037
   (1,3)      -0.0036
   (2,3)      -0.0034
   (3,3)      -0.0158
   (4,3)      -0.0203
   (5,3)      -0.0169
   (6,3)      -0.0061
   (7,3)       0.0192
   (8,3)       0.0273
   (9,3)       0.0497
  (10,3)       0.0357
  (11,3)       0.0675
  (12,3)      -0.0009
  (13,3)       0.0040
  (14,3)      -0.1001
  (15,3)      -0.0475
  (16,3)       0.0002
  (17,3)       0.0029
  (18,3)      -0.0192
  (19,3)      -0.0052
  (20,3)      -0.0820
  (21,3)      -0.0871
  (22,3)      -0.0499
  (23,3)      -0.0548
  (24,3)      -0.0588
  (25,3)       0.1615
  (26,3)       0.1362
  (27,3)       0.1492
  (28,3)       0.1266
  (29,3)       0.1050
  (30,3)       0.0565
  (31,3)       0.0041
  (32,3)      -0.0001
  (33,3)      -0.0112
  (34,3)      -0.0105
  (35,3)      -0.0048
  (36,3)       0.0005
  (90,3)      -0.0312
  (91,3)      -0.0479
  (92,3)      -0.0275
  (93,3)      -0.0274
  (94,3)      -0.0276
  (95,3)      -0.0284
  (96,3)      -0.0019
  (97,3)       0.0435
  (98,3)      -0.0143
  (99,3)       0.0351
 (100,3)       0.0015
 (101,3)       0.0245
   (1,4)       0.0096
   (2,4)      -0.0146
   (3,4)      -0.0317
   (4,4)      -0.0480
   (5,4)      -0.0452
   (6,4)      -0.0500
   (7,4)       0.0030
   (8,4)      -0.0126
   (9,4)      -0.0173
  (10,4)      -0.0061
  (11,4)      -0.0164
  (14,4)      -0.0899
  (15,4)      -0.0988
  (16,4)      -0.0068
  (17,4)      -0.0163
  (18,4)      -0.0238
  (19,4)       0.0100
  (20,4)      -0.0873
  (21,4)      -0.0405
  (22,4)      -0.1179
  (23,4)      -0.1305
  (24,4)      -0.1540
  (25,4)      -0.0648
  (26,4)      -0.0618
  (27,4)      -0.0634
  (28,4)      -0.0658
  (29,4)      -0.0677
  (30,4)      -0.0475
  (31,4)      -0.0524
  (32,4)      -0.0428
  (33,4)      -0.0454
  (34,4)      -0.0879
  (35,4)      -0.0706
  (36,4)      -0.0683
  (48,4)      -0.0059
  (49,4)       0.0018
  (50,4)       0.0017
  (51,4)      -0.0005
  (90,4)       0.0447
  (91,4)       0.0510
  (92,4)       0.0390
  (93,4)       0.0593
  (94,4)       0.0293
  (95,4)       0.0111
  (96,4)       0.0271
  (97,4)       0.0325
  (98,4)       0.0351
  (99,4)       0.0176
 (100,4)       0.0395
 (101,4)       0.0425
 (102,4)      -0.0361
 (103,4)      -0.0069
   (1,5)       0.0303
   (2,5)      -0.0011
   (3,5)      -0.0063
   (4,5)      -0.0144
   (5,5)      -0.0044
   (6,5)      -0.0014
   (7,5)       0.0077
   (8,5)       0.0321
   (9,5)       0.0673
  (10,5)       0.0653
  (11,5)       0.0627
  (14,5)      -0.0852
  (15,5)      -0.0420
  (16,5)       0.0085
  (17,5)       0.0094
  (18,5)       0.0036
  (19,5)       0.0149
  (20,5)      -0.0766
  (21,5)      -0.0540
  (22,5)      -0.0943
  (23,5)      -0.1194
  (24,5)      -0.1136
  (25,5)       0.1361
  (26,5)       0.0990
  (27,5)       0.1184
  (28,5)       0.0984
  (29,5)       0.0827
  (30,5)       0.0097
  (31,5)       0.0034
  (32,5)       0.0014
  (33,5)      -0.0080
  (34,5)       0.0010
  (35,5)       0.0028
  (36,5)       0.0066
  (48,5)       0.0763
  (49,5)      -0.1196
  (50,5)      -0.1216
  (51,5)      -0.0006
  (90,5)      -0.0534
  (91,5)      -0.0576
  (92,5)      -0.0553
  (93,5)      -0.0271
  (94,5)       0.0083
  (95,5)      -0.0030
  (96,5)      -0.0080
  (97,5)      -0.0084
  (98,5)      -0.0075
  (99,5)      -0.0072
 (100,5)      -0.0055
 (101,5)      -0.0182
 (102,5)      -0.0294
 (103,5)      -0.0385
   (1,6)       0.0336
   (2,6)      -0.0169
   (3,6)      -0.0049
   (4,6)      -0.0047
   (5,6)      -0.0046
   (6,6)      -0.0036
   (7,6)      -0.0061
   (8,6)      -0.0016
   (9,6)      -0.0086
  (10,6)      -0.0017
  (11,6)      -0.0004
  (14,6)       0.0130
  (15,6)      -0.0007
  (16,6)      -0.0465
  (17,6)      -0.0559
  (18,6)      -0.0625
  (19,6)      -0.0464
  (20,6)       0.0106
  (21,6)       0.0447
  (22,6)       0.0261
  (23,6)       0.0170
  (24,6)       0.0187
  (25,6)       0.0202
  (26,6)       0.0205
  (27,6)       0.0116
  (28,6)       0.0114
  (29,6)       0.0052
  (30,6)       0.0442
  (31,6)       0.0208
  (32,6)       0.0118
  (33,6)      -0.0186
  (34,6)      -0.0202
  (35,6)      -0.0279
  (36,6)      -0.0378
  (48,6)       0.2236
  (49,6)      -0.4827
  (50,6)      -0.4924
  (51,6)       0.1050
  (90,6)      -0.0061
  (91,6)      -0.0042
  (92,6)      -0.0166
  (93,6)      -0.0146
  (94,6)      -0.0427
  (95,6)      -0.0607
  (96,6)       0.0013
  (97,6)       0.0183
  (98,6)       0.0339
  (99,6)       0.0306
 (100,6)       0.0540
 (101,6)      -0.0007
 (102,6)       0.1613
 (103,6)       0.0431
  (37,7)      -0.0087
  (38,7)      -0.0120
  (39,7)      -0.0161
  (40,7)      -0.0172
  (41,7)      -0.0122
  (42,7)      -0.0153
  (43,7)      -0.0151
  (44,7)      -0.0092
  (45,7)      -0.0113
  (46,7)      -0.0068
  (47,7)      -0.0022
  (48,7)       0.0047
  (49,7)       0.0059
  (50,7)       0.0046
  (51,7)       0.0049
  (52,7)       0.0114
  (53,7)       0.0122
  (54,7)       0.0135
  (55,7)      -0.0117
  (56,7)      -0.0094
  (57,7)      -0.0097
  (58,7)      -0.0095
  (59,7)      -0.0111
  (60,7)      -0.0095
  (61,7)      -0.0105
  (62,7)      -0.0081
  (63,7)      -0.0118
  (64,7)       0.0029
  (65,7)       0.0051
  (66,7)       0.0011
  (67,7)       0.0028
  (68,7)      -0.0080
  (69,7)      -0.0082
  (70,7)       0.0045
  (71,7)       0.0049
  (72,7)       0.0173
  (73,7)       0.0155
  (74,7)       0.0119
  (75,7)       0.0152
  (76,7)       0.0124
  (77,7)       0.0126
  (78,7)       0.0112
  (79,7)       0.0140
  (80,7)       0.0129
  (81,7)       0.0088
  (82,7)       0.0074
  (84,7)       0.0139
  (85,7)       0.0139
  (86,7)      -0.0049
  (87,7)      -0.0012
  (88,7)      -0.0051
  (89,7)      -0.0013
  (90,7)       0.0124
  (91,7)       0.0120
  (92,7)       0.0128
  (93,7)       0.0091
  (94,7)       0.0080
  (95,7)       0.0094
  (96,7)       0.0199
  (97,7)       0.0293
  (98,7)       0.0296
  (99,7)       0.0311
 (100,7)       0.0229
 (101,7)       0.0263
 (102,7)       0.0238
 (103,7)       0.0214
  (37,8)      -0.0417
  (38,8)      -0.0246
  (39,8)      -0.0094
  (40,8)      -0.0058
  (41,8)       0.0097
  (42,8)       0.0010
  (43,8)       0.0035
  (44,8)       0.0116
  (45,8)       0.0167
  (46,8)       0.0243
  (47,8)       0.0275
  (48,8)       0.0217
  (49,8)       0.0038
  (50,8)       0.0030
  (51,8)       0.0032
  (52,8)       0.0103
  (53,8)       0.0111
  (54,8)       0.0122
  (55,8)      -0.0008
  (56,8)       0.0073
  (57,8)       0.0074
  (58,8)       0.0123
  (59,8)       0.0135
  (60,8)       0.0012
  (61,8)      -0.0068
  (62,8)      -0.0029
  (63,8)      -0.0038
  (64,8)      -0.0593
  (65,8)      -0.0780
  (66,8)      -0.0699
  (67,8)      -0.0410
  (70,8)       0.0224
  (71,8)       0.0217
  (72,8)      -0.0013
  (73,8)       0.0025
  (74,8)       0.0082
  (75,8)       0.0084
  (76,8)       0.0096
  (77,8)       0.0014
  (78,8)       0.0016
  (79,8)       0.0015
  (80,8)       0.0034
  (81,8)      -0.0060
  (82,8)      -0.0213
  (84,8)       0.0125
  (85,8)       0.0125
  (86,8)      -0.0443
  (87,8)      -0.0286
  (88,8)      -0.0314
  (89,8)      -0.0286
  (90,8)      -0.0092
  (91,8)      -0.0200
  (92,8)      -0.0230
  (93,8)      -0.0218
  (94,8)      -0.0318
  (95,8)      -0.0246
  (96,8)      -0.0292
  (97,8)      -0.0059
  (98,8)      -0.0000
  (99,8)       0.0131
 (100,8)       0.0077
 (101,8)       0.0046
 (102,8)       0.0110
 (103,8)       0.0102
  (37,9)      -0.0057
  (38,9)      -0.0057
  (39,9)      -0.0045
  (40,9)      -0.0050
  (41,9)      -0.0040
  (42,9)      -0.0032
  (43,9)      -0.0032
  (44,9)      -0.0004
  (45,9)      -0.0036
  (46,9)      -0.0027
  (47,9)      -0.0004
  (48,9)       0.0004
  (49,9)       0.0039
  (50,9)       0.0032
  (51,9)       0.0032
  (52,9)       0.0000
  (53,9)      -0.0006
  (55,9)      -0.0140
  (56,9)      -0.0127
  (57,9)      -0.0131
  (58,9)      -0.0130
  (59,9)      -0.0130
  (60,9)      -0.0081
  (61,9)      -0.0109
  (62,9)      -0.0078
  (63,9)      -0.0112
  (64,9)       0.0100
  (65,9)       0.0081
  (66,9)       0.0076
  (67,9)       0.0119
  (68,9)      -0.0078
  (69,9)      -0.0077
  (70,9)       0.0006
  (71,9)       0.0007
  (72,9)       0.0145
  (73,9)       0.0129
  (74,9)       0.0115
  (75,9)       0.0145
  (76,9)       0.0135
  (77,9)       0.0161
  (78,9)       0.0183
  (79,9)       0.0202
  (80,9)       0.0179
  (81,9)       0.0148
  (82,9)       0.0115
  (86,9)      -0.0001
  (87,9)      -0.0021
  (88,9)      -0.0027
  (89,9)      -0.0022
  (90,9)       0.0134
  (91,9)       0.0127
  (92,9)       0.0123
  (93,9)       0.0143
  (94,9)       0.0116
  (95,9)       0.0097
  (96,9)       0.0147
  (97,9)       0.0154
  (98,9)       0.0154
  (99,9)       0.0157
 (100,9)       0.0153
 (101,9)       0.0144
 (102,9)       0.0099
 (103,9)       0.0089
  (83,10)      0.0413
  (84,10)      0.0432
  (85,10)      0.0432
  (86,10)     -0.0189
  (87,10)     -0.0189
  (88,10)     -0.0189
  (89,10)     -0.0189
 (104,10)     -0.0191
 (105,10)     -0.0192
 (106,10)     -0.0185
 (107,10)     -0.0191
 (108,10)     -0.0192
 (109,10)      0.0280
 (110,10)      0.0273
 (111,10)      0.0205
 (112,10)      0.0261
 (113,10)      0.0207
 (114,10)      0.0198
 (115,10)      0.0213
 (116,10)      0.0532
 (117,10)      0.0372
 (118,10)      0.0284
 (119,10)      0.0081
 (129,10)     -0.0197
 (130,10)     -0.0196
 (131,10)     -0.0191
 (132,10)     -0.0190
 (133,10)     -0.0189
 (134,10)     -0.0075
 (135,10)     -0.0061
 (136,10)     -0.0070
 (137,10)     -0.0047
 (138,10)     -0.0058
  (83,11)     -0.0040
  (84,11)     -0.0040
  (85,11)     -0.0040
 (116,11)     -0.0054
 (117,11)     -0.0040
 (118,11)     -0.0028
 (119,11)      0.0027
 (120,11)      0.0025
 (121,11)     -0.0174
 (122,11)     -0.0136
 (123,11)     -0.0105
 (124,11)     -0.0095
 (125,11)     -0.0088
 (126,11)      0.0055
 (127,11)      0.0069
 (128,11)      0.0087

Compute muscle-tendon lengths

Inputs

Outputs

lengths = das3('Musclelengths', x)
lengths =

    0.1871
    0.1534
    0.1300
    0.1126
    0.1021
    0.1119
    0.1230
    0.1383
    0.1631
    0.1940
    0.2178
    0.1688
    0.1577
    0.1327
    0.0965
    0.1020
    0.1211
    0.0945
    0.1224
    0.0755
    0.1475
    0.0966
    0.0728
    0.0610
    0.2061
    0.2061
    0.2112
    0.2021
    0.1895
    0.1640
    0.1237
    0.1049
    0.0821
    0.0492
    0.0537
    0.0418
    0.1948
    0.1653
    0.1433
    0.1169
    0.1055
    0.1448
    0.1603
    0.1365
    0.1436
    0.1287
    0.1162
    0.1765
    0.1319
    0.1080
    0.1884
    0.1589
    0.1736
    0.1870
    0.0927
    0.1758
    0.1508
    0.1392
    0.1251
    0.1066
    0.1066
    0.1199
    0.0814
    0.1201
    0.1453
    0.1356
    0.1068
    0.0836
    0.0897
    0.1053
    0.0833
    0.0635
    0.0904
    0.1018
    0.1064
    0.1094
    0.1330
    0.1278
    0.0837
    0.1059
    0.0540
    0.0411
    0.3620
    0.3331
    0.3331
    0.3209
    0.3251
    0.3548
    0.3497
    0.1941
    0.2290
    0.2540
    0.3066
    0.2737
    0.2386
    0.1674
    0.1945
    0.1996
    0.1932
    0.1968
    0.1790
    0.1661
    0.1181
    0.1652
    0.1889
    0.0737
    0.1127
    0.1560
    0.1774
    0.1688
    0.1352
    0.1321
    0.0998
    0.0869
    0.0857
    0.3220
    0.3130
    0.3007
    0.1620
    0.0901
    0.0439
    0.0410
    0.0406
    0.0410
    0.0432
    0.0500
    0.0451
    0.0397
    0.2424
    0.2450
    0.2488
    0.2090
    0.2050
    0.0247
    0.0241
    0.0275
    0.0526
    0.0379

Find muscle name

Inputs

Outputs

number = 1;
name = das3('Musclename', number)
name =

trapscap1

Find if muscle crosses the glenohumeral joint

Inputs

Outputs

crossGH_flag = das3('crossGH', number)
crossGH_flag =

     0

Next: Model reference
Previous: Simtk project and files included in this release
Home: Main