Computational Neuroscience Models of the Basal Ganglia by V. Srinivasa Chakravarthy & Ahmed A. Moustafa

Author:V. Srinivasa Chakravarthy & Ahmed A. Moustafa
Language: eng
Format: epub
Publisher: Springer Singapore, Singapore

8.2.7 Grip Force During Transient Friction Change (Gupta et al., 2013a, 2013b)

Previous studies failed to present a consolidated model to explain the grip force variability that can be used to explain the abnormalities demonstrated in PD patients. The following model provide a more comprehensive GF production mechanism under friction changes in healthy subjects that serves as a backbone for the modeling the GF in PD patients and hence discussed in detail.

As discussed earlier, the GF and LF produced are tightly coupled to the size, weight, texture, surface curvature of the object, and the friction (between the object surface and interacting finger boundary). In a controlled experimental setup, the physical properties of the object are kept constant and the only sources of variation are the object–finger friction and neural control.

Therefore, a model of PGLT was developed to account for the GF and LF variability due to subjective friction differences in healthy subjects (Gupta et al., 2013a, 2013b).

Gupta et al. (2013a, 2013b) proposed a computational model to demonstrate the GF–LF variability in high friction (when the grip formed was using dry fingers) and low friction (when the fingers were wetted >2 min, as described by Johansson and Westling (1984) to prevent μ changes) conditions. The model (Fig. 8.3) comprised of two PID force controllers—F G controller and F L controller—that generate F G and F L, respectively. The F G controller received slip information as the error input, where slip is defined as the relative motion between the finger and the object. Therefore, any velocity difference between the fingers and the object led to an increase in F G that in turn tries to prevent the slip. The lift force controller receives difference between the target position and the actual object position as the input. The dynamics of the object–finger interaction is modeled in plant that generates the acceleration, velocity, and position profiles for both the finger and the object.

Fig. 8.3An overview of the Gupta et al. (2013a, 2013b) model. The grip force controller (F G controller) receives the slip information (as absolute difference between the finger velocity and object velocity) to generate a grip error (E G) that is converted to the grip force (F G). The lift force controller (F L controller) receives the difference between the target position and the current position as the input and outputs the lift force (F L). Finger–object interaction module based on the F G and F L generates the position, velocity, and accelerations for both finger (subscript fin) and object (subscript o). Modified from Gupta et al. (2013a, 2013b)

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.