Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation
Figure 7
Individual strings contribute significantly to the functionality of the chosen models.
(a) model (i) in Fig. 4e and (c) model (iv) in Fig. 4e with string members numbered. (b) Each string member is removed one at a time from model (i) and cross validation errors are computed. The intact model (with no string removed) exhibits the least cross validation error. (d) Each string member is removed one at a time from model (iv) and cross validation errors are computed. On removal of strings 22 or 24, the error decreases. This suggests that connection between middle and distal slips should be absent. Removing string 7 or 10 from the model in (c) does not alter the cross validation error from that of the intact model. This is because these strings do not get taut when cross validating load sets are used. Visual inspection further shows that these strings barely get taut when the model is actuated with the informative load sets used for its evolution. This suggests that strings 7 and 10 do not participate in the network which explains why the training set error for this model is larger (Table 2) compared to that of model (i) in (a).