Feedforward control strategies of subjects with transradial amputation in planar reaching

Abstract

Abstract-The rate of upper-limb amputations is increasing, and the rejection rate of prosthetic devices remains high. People with upper-limb amputation do not fully incorporate prosthetic devices into their activities of daily living. By understanding the reaching behaviors of prosthesis users, researchers can alter prosthetic devices and develop training protocols to improve the acceptance of prosthetic limbs. By observing the reaching characteristics of the nondisabled arms of people with amputation, we can begin to understand how the brain alters its motor commands after amputation. We asked subjects to perform rapid reaching movements to two targets with and without visual feedback. Subjects performed the tasks with both their prosthetic and nondisabled arms. We calculated endpoint error, trajectory error, and variability and compared them with those of nondisabled control subjects. We found no significant abnormalities in the prosthetic limb. However, we found an abnormal leftward trajectory error (in right arms) in the nondisabled arm of prosthetic users in the vision condition. In the no-vision condition, the nondisabled arm displayed abnormal leftward endpoint errors and abnormally higher endpoint variability. In the vision condition, peak velocity was lower and movement duration was longer in both arms of subjects with amputation. These abnormalities may reflect the cortical reorganization associated with limb loss.

Incidental Learning and Explicit Recall in Upper Extremity Prosthesis Use: Insights Into Functional Rehabilitation Challenges

“…These findings showed that during motor planning of the right arm with a prosthesis, users show expected left parietofrontal activation, and unexpected right parietooccipital activation, which has been attributed to visuospatial aspects of action (Van Overwalle & Baetens, 2009). This fits with the proposal that increased visuospatial demands of motor control is seen in amputees (Metzger et al, 2010), and may relate to the findings seen in this study. In the cortex, studies have demonstrated that principally motor cortex, lateral premotor, and the supplementary motor areas (SMA) are commonly involved in motor learning (Hikosaka, Nakamura, Sakai, & Nakahara, 2002;Velasques et al, 2007;Wolpert, Ghahramani, & Flanagan, 2001).…”

“…However, controlling the terminal device becomes more difficult to learn because of fine motor adjustments. Studies by Metzger et al (2010) and Bouwsema et al (2014) found similar results in motor control. The modified SRTT implemented in this study may be more sensitive, when compared to the original SRTT, to errors arising from poverty of controlling the disc within the terminal device.…”

Comparison of range-of-motion and variability in upper body movements between transradial prosthesis users and able-bodied controls when executing goal-oriented tasks

“…The key to control when using a transradial prosthesis is to properly manage the remaining DoFs [5], and studies indicate that the CNSs of prosthesis users create accurate internal models of the affected arm through residual sensory feedback and utilize adapted motor control strategies (i.e., coordinated joint torques and geometry) for task execution [16–19]. Importantly, evidence suggests that although upper limb prosthesis users adapt to novel task environments, they demonstrate increased motor variability, but dedicated experience and training with the device may facilitate long-term motor adaptation and increased kinematic repeatability [18, 20, 21].…”

Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping

“…Recent studies demonstrated that prosthesis users, similar to able-bodied subjects, might employ internal models for feedforward control of prostheses with no somatosensory feedback (Lum et al 2014;Metzger et al 2010;Weeks et al 2000). However, there is only a single study (Saunders and Vijayakumar 2011) addressing the role of feedforward and feedback processes when using a closed-loop prosthesis.…”