Adaptation of arm trajectory during continuous drawing movements in different dynamic environments

Abstract

Human subjects can readily adapt their movement trajectories to different dynamic or visuomotor environments. The focus of the current study was to determine whether subjects could simultaneously adapt to multiple dynamic environments. Subjects ( n=5) drew ellipses continuously for 70 s using a torquable manipulandum under six distinct dynamic conditions, representing the combination of load type (spring, viscous, and inertia) and load direction (assisting and opposing). Each subject performed two control, ten load, and five washout trials. A significant effect of force condition on the trajectory of the movement was found in 26 of 30 cases (6 conditions x 5 subjects); the magnitude of the distortion differed across the conditions. The extent of adaptation also differed across the loads. Opposing inertia and viscosity led to fast adaptation. However, assisting inertia and viscosity were associated with relatively slow adaptation. The results of adaptation to the stiffness conditions were not consistent. Following sudden removal of the load we saw an additional disturbance of the trajectory (after-effect), which was often the mirror image of the original distortion. The shape and size of the after-effect were different across load conditions. These results show that human subjects can adapt to a variety of different dynamic transformations and that the time-course of adaptation is dependent on both the state space and the direction of the load.

Quantifying drug-induced dyskinesias in the arms using digitised spiral-drawing tasks

“…Various drawing tasks have been studied (Fukushi and Ashe, 2003;Lacquaniti, 1989;Mergl et al, 1999;Moran and Schwartz, 1999;Reina and Schwartz, 2003) and clinically, scoring the patient's drawing performance is a convenient and effective way to assess the severity of motor dysfunctions such as tremor of the upper limb (Bain et al, 1993). Digitising tablets can be used to quantify normal (Lacquaniti et al, 1987;Mergl et al, 1999) and tremulous arm movements (Elble et al, 1996;Erasmus et al, 2001;Pullman, 1998).…”

Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain–computer interface algorithms

“…For example, it is well known that humans are capable of adapting to specific types of novel visuo-motor distortions (Held & Freedman, 1963; Ghahramani et al , 1996; Bock et al , 2003) or force field environments (Flash & Gurevich, 1991; Lackner & Dizio, 1994; Shadmehr & Mussa-Ivaldi, 1994). Also, it has been shown that humans can adapt to some types of perturbations more readily than others (Cunningham, 1989; Krakauer et al , 2000; Fukushi & Ashe, 2003; Hwang et al , 2003). If the decoded movement can be viewed as a distortion of the desired movement, then it may well be true that certain decoders may be more “learnable” than others.…”

A Semi-passive Planar Manipulandum for Upper-Extremity Rehabilitation

“…38,32,48 For this reason, some motorized robots have been adapted to incorporate resistive brakes or challenge-based control algorithms to increase the difficulty of training or resist the user’s movements. 31,22,6,3,14,4 However, because these robotic devices use active actuators, they are typically expensive, bulky, and can even be potentially unsafe if not programmed correctly, which prevents them from being used in the comfort of a patients home. Thus, patients often rely on low-cost passive devices (e.g., elastic bands, weights, etc.…”