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Bar and Angle Iron All figures show the No. 2 Bender with tooling needed for the specific bend. All tooling sold separately. Bending Sharp Bends or “U” Bolts Sharp Cornered “U” Bolts are bent on the same feature of the bender as shown for bending ornamental iron. Product description: this scroll paper towel holder boasts a silver metal wire design that'll look fantastic in your kitchen. Howron Industries Metal Benders. A synergy- based hand control is encoded in human motor cortical areas. In post review discussion, all reviewers agreed with the suggestion that noise ceilings should be reported. Reviewer #1: The study . The main finding is that the activity patterns of two left - out postures can be better discriminated when using 5 regressors extracted from kinematic synergies than 5 regressors reflecting the unsigned displacement of individual fingers or 5 regressors picked from features of EMG recording of 5 different hand muscles. The conclusion of the study are largely overlapping with that of an earlier paper from our lab (Ejaz et al., 2. The current version of the paper, however, has a number of weaknesses that certainly would need addressing. The alternative models (individual fingers and muscle) give the appearance of straw men. Subjects were asked to shape the right hand as if to grasp and use a large number of familiar objects. The chosen objects typically are held with a variety. Postural Hand Synergies for Tool Use Marco Santello, Martha Flanders, and John F. Soechting Neuroscience Department, University of Minnesota, Minneapolis, Minnesota 55455. The individual finger uses the L1 norm of movement of each finger – so in contrast to the kinematic synergy model, it does not distinguish between finger flexion and finger extension. This decision appears to be somewhat arbitrary. So, it leaves the reader with the question of whether there is something special about taking the absolute value, or about the specific rotation of these 5 factors in representational space. A more convincing line of investigation would be to try to use optimisation to rotated the 5 linear factors in the kinematic space as to get the best possible decoding performance, and then test the closeness of this solution with the one provided by the kinematic synergy model. The Reviewer is absolutely correct in stating that finger flexion and extension fully deserve to be kept as distinct as possible. As a matter of fact, our individual digit model was actually obtained by summing the individual joint angles for each digit, though in the description in the text we erroneously reported that it was the L1- norm of each digit. Consequently, our original individual digit model already distinguished positive and negative joint angles (with respect to the resting posture), thus preserving the difference between flexion and extension. We apologize for this erroneously reported piece of information. Matters are worse with the muscle model. The authors recorded 5 muscles only, despite the fact that in our experience it is feasible to get 1. Three hand postural synergies were identified through a principal. Flanders M, Soechting JF. Postural hand synergies for tool use. Mapping Grasps from the Human Hand to the DEXMART Hand by Means of Postural Synergies and. Postural hand synergies for tool use. Decoding the neural mechanisms of human tool use. PubMed journal article Postural hand synergies for tool us was found in Unbound MEDLINE. Download PubMed App to iPhone, iPad, Android smartphone and tablet. Titre du document / Document title POSTURAL HAND SYNERGIES FOR TOOL USE Auteur(s) / Author(s) SANTELLO M.; FLANDERS M.; SOECHTING J. F.; Revue / Journal Title. Postural Hand Synergies for Tool Use Materials and Methods. Marco Santello, Martha Flanders, John F Soechting; 1998; View PDF; Cite; Save; Abstract. Subjects were asked to shape the right hand as if to grasp and use a large number of familiar objects. The chosen objects typically are held with a variety of grips. Flanders M, Soechting JF: Postural hand synergies for tool use. J Neurosci 1998,18(23). Journal of NeuroEngineering and Rehabilitation ISSN. Ejaz et al., 2. 01. These may not always reflect individual muscles, but that is hardly important if we only want to obtain a representative picture of the space of muscle activity. Previous reports indicate that a reliable gesture discrimination can be achieved from seven (Weiss & Flanders, 2. Shyu et al., 2. 00. Ganesh et al., 2. Ahsan et al., 2. 01. EMG data are notoriously prone to artifacts such as cross- talk or amplitude cancellation (i. In our study, both the number of recorded muscles and the analyses performed were able to guarantee a reliable discrimination between gestures, as indicated by the rank accuracy measure, which was well above the chance level for each subject (Supplementary file 1. M and Figure 6). In most advanced EMG devices, the number of acquisition sites on the forearm can raise dramatically up to 1. Gazzoni et al., 2. Muceli et al., 2. However, due to the above mentioned cross- talk, the effect of the potential benefit that could derive from an increased number of EMG electrodes is still much debated: indeed multi- channel setups show a high collinearity, since many channels inevitably record the same muscles. As a matter of fact, a recent work that directly compared multiple channel setups – using either 6, 8, 1. Muceli et al., 2. Nevertheless, in order to explore directly the Reviewer’s critique, we tested an independent sample of four healthy young subjects who performed the very same task used in our current paper to verify the impact of the number of EMG channels on the . To this aim, for each subject we acquired six runs, each comprising twenty trials of delayed grasp- to- use motor acts towards visually- presented objects. Data were acquired using a Bagnoli 1. EMG recording device (Delsys Inc, Natick, MA, USA). Sixteen electrodes were placed on the hand and forearm using the same placement adopted in our protocol (see revised Methods and Appendix figure 1) as well as in three distinct protocols with different spatial resolutions (Bitzer and van der Smagt, 2. Ganesh et al., 2. Ejaz et al., 2. 01. To obtain an overall estimation of the impact of the number of EMG recording sites and different preprocessing steps, data were analyzed using two distinct procedures: the first suggested by the Reviewer (Ejaz et al., 2. Methods. Both preprocessing procedures were assessed with a leave- one- out cross- validation algorithm. Using the first procedure (Ejaz et al., 2. EMG channels (acquired at 1,0. Hz) were de- trended, rectified, and low- pass filtered (fourth- order Butterworth filter, 4. Hz). The time series from each gesture and channel were later averaged over a 2. We obtained twenty 1. Then, we tested the discriminability of each individual movement (probe element) from each run against all the movement vectors averaged across the five remaining runs (rest dataset). This was performed with a leave- one- out rank accuracy procedure (Mitchell et al., 2. Mahalanobis distance. If the distance between the probe element and the vector from the rest dataset, which represents the same gesture, is lower than the distances between the probe vector and the other elements of the rest dataset, one may state that the element can be discriminated. The accuracies were tested against null distributions of 1. This rank accuracy procedure provides a measure of the quality of each individual channel configuration: the higher the accuracy, the more informative the configuration. Since we wanted to estimate the discriminability as a function of the number of channels, we performed two tests, whose results are shown in Appendix figure 2. First, we generated all the possible configurations that could be obtained by choosing the channels randomly. The rank accuracy procedure was performed for each of these channel configurations. The result of this procedure is shown in Appendix figure 2 (red line). Second, we selected four subsamples of electrodes (displayed in Appendix figure 1; electrodes 1- 4, 6- 8, 1. Ejaz et al., 2. 01. Results for each configuration, averaged across subjects, are represented by the orange dots in Appendix figure 2. In the feature- based procedure, we computed, for each trial, eighty- two features from each channel, as described in the Methods section of our manuscript. Each individual gesture was then described as a combination of five PCs (muscle synergies), extracted from the features pooled across channels. Subsequently, a machine learning procedure based on a rank accuracy measure was employed to test to what extent the gestures could be discriminated based on the five muscle synergies (see Methods). As done with the procedure previously described, we tested all configurations that could be obtained by randomly selecting 1 to 1. Appendix figure 2, blue line), as well as four subsamples according to the setups described above (Appendix figure 2, light blue dots). The results show that, for the feature- based procedure, the accuracy increases as a function of the number of electrodes, reaching a peak with 1. The accuracy obtained with the setup adopted in our current paper was 7. For the procedure described in Ejaz et al. In these data, the accuracy for the configuration of five channels adopted in our paper was 6. A possible explanation for this finding may be related to the different experimental designs: when more complex gestures are considered (as in our design), data are probably more prone to electrode shifts or amplitude cancellation artifacts, whose impact increases with the number of electrodes in the EMG acquisition setup. Moreover, the better results achieved by using the feature- based approach, as compared to the procedure suggested by the Reviewer, may indicate that feature extraction represent a better technique for exploiting the advantages of EMG setups with higher spatial resolution, consistent with the data already present in the literature (Hudgins et al., 1. Zecca et al., 2. 00. Despite the better discrimination accuracy obtained with the feature- based procedure using a higher number of channels, we found that the accuracy values using a . Therefore, we conclude that the worst performance of the . For the reasons detailed below (Point 1c), we would expect that even high- density EMG data may not guarantee a better fit to brain signals than electrode configurations with a lower dimensionality. The extracted features from the EMG signals appear obscure to me; and ultimately, the ability to distinguish between postures based on this data is very bad, indicating that most of these numbers reflect noise. Given these large a- priori differences in the quality of the models, I think any subsequent difference in how well f. MRI activity patterns can be predicted become utterly unconvincing. So I think the authors need to work harder on trying to equate the reliability of their models (for a possible method, see Ejaz et al. EMG data are complex signals, as they represent indirectly the activity of one or more underlying muscles (Farina et al., 2. Reaz et al., 2. 00. Farina et al., 2. However, a well- consolidated assumption is that the instantaneous value of the EMG signal contains little or no information (Zecca et al., 2. In addition, during complex movements the temporal structure of the EMG signal varies to a great deal. 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