Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation

Abstract

Daily interaction with the environment consists of moving with or without objects. Increasing interest in both types of movements drove the creation of computational models to describe reaching movements and, later, to describe a simplified version of object manipulation. The previously suggested models for object manipulation rely on the same optimization criteria as models for reaching movements, yet there is no single model accounting for both tasks that does not require reminimization of the criterion for each environment. We suggest a unified model for both cases: minimum acceleration with constraints for the center of mass (MACM). For point-to-point reaching movement, the model predicts the typical rectilinear path and bell-shaped speed profile as previous criteria. We have derived the predicted trajectories for the case of manipulating a mass-on-spring and show that the predicted trajectories match the observations of a few independent previous experimental studies of human arm movement during a mass-on-spring manipulation. Moreover, the previously reported "unusual" trajectories are also well accounted for by the proposed MACM. We have tested the predictions of the MACM model in 3 experiments with 12 subjects, where we demonstrated that the MACM model is equal or better (Wilcoxon sign-rank test, P < 0.001) in accounting for the data than three other previously proposed models in the conditions tested. Altogether, the MACM model is currently the only model accounting for reaching movements with or without external degrees of freedom. Moreover, it provides predictions about the intermittent nature of the neural control of movements and about the dominant control variable.

The minimum transition hypothesis for intermittent hierarchical motor control

“…Figure 2 illustrates the motor control hierarchical nature and the location of the MTH within this system. Numerous criteria were proposed to account for the trajectory formation in reaching movements (e.g., Abend et al, 1982; Flash and Hogan, 1985; Uno et al, 1989; Harris and Wolpert, 1998) and several studies have proposed various criterions for the manipulation of mass on a spring (Dingwell et al, 2004; Svinin et al, 2005, 2006; Leib and Karniel, 2012). The desired trajectory is expected to be found at the high level neural activity of the CNS, and in a well-practiced conditions, the actual arm trajectory is expected to be similar to the desired trajectory.…”

“…The desired trajectory is expected to be found at the high level neural activity of the CNS, and in a well-practiced conditions, the actual arm trajectory is expected to be similar to the desired trajectory. Here we discuss intermittent control and in this context the minimum acceleration criterion with constraints [MACC, (Ben-Itzhak and Karniel, 2008; Leib and Karniel, 2012)], which predicts intermittent control signals, is probably the best candidate for the desired trajectory; however, other desired trajectories could be considered. One should note that the MACC predicts a continuous arm trajectory with a bell shaped speed profile, however, the predicted Jerk signal, namely the third time derivative of the path is a piecewise constant function of time and therefore it practically predicts intermittent control signals.…”

“…The MTH combined with the MACC hypothesis predict specific timing of the transitions in the muscles commands occurring during reaching movements and flexible object manipulation (Ben-Itzhak and Karniel, 2008; Leib and Karniel, 2012). …”

Smart switching in feedforward control of grip force during manipulation of elastic objects

“…the velocity in the former example, the temperature of the thermostat in the second and moisture in the last. Such control systems have many benefits, including economy in control effort (Ben-ltzhak and Karniel, 2008; Karniel, 2011; Leib and Karniel, 2012) and the ability to stabilize otherwise unstable systems (Margaliot and Liberzon, 2006; Lin and Antsaklis, 2009; Liberzon, 2003; Wicks et al, 1998).…”

Predictability and Robustness in the Manipulation of Dynamically Complex Objects

“…Other studies have focused on the role of visual and haptic information to learn complex manipulation (Huang et al 2002, 2007; Danion et al 2012). Yet other research examined the displacement of a linear mass-spring object and proposed optimization criteria, such as generalized kinematic smoothness (Dingwell et al 2004), accuracy and effort (Nagengast et al 2009), and minimum acceleration with constraints on the center of mass (Leib and Karniel 2012). While interesting insights have been gained, most studies implicitly or explicitly assume that the human has, or has to learn an internal model of the manipulated object.…”