A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy

Abstract

Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal–plantar and inversion–eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different “human” hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots.

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

“…On the contrary, adults showed a perfect bell-shaped velocity profile and, consequently, they are not influenced by gravity. These results are in line with [18], in which authors established that, in adults subjects, the velocity profile is not affected by gravity. Thus, we can speculate that the differences between children and adults could be due to higher force levels that can be exerted by adults at the ankle, implying a negligible effect of gravity.…”

“…Michmizos and Krebs evaluated the relationship between the speed and the accuracy in both dorsi-plantar (DP) and inversion-eversion (IE) movements performed by adults, assessing the possibility to describe this relation with Fitt's law [17]. The same authors, in [18], compared several models of speed profile in ankle pointing movements, finding that the best fitting models were those already used for upper limbs during pointing movements.…”

Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation

“…We then assessed their explicit learning by examining how the game parameters adapted to their performance and their implicit learning by analyzing how the distribution of their RT changed with therapy. Our evaluation was supported by our recent studies on the ankle sensorimotor control of young healthy subjects [25, 26, 29, 30]. …”

“…In our second, mesoscopic study, we found a remarkable similarity between the models that described the speed profiles of unimpaired ankle pointing movements and the ones previously found for the upper extremities both during arm reaching and wrist pointing movements. [26]. In our third, microscopic study, we found that the reaction time (RT) measured in both DP and IE ankle movements increased with the number of stimuli at an equal pace, as would be predicted by Hick-Hyman law in UE [27, 28].…”

Testing the concurrent validity of a naturalistic upper extremity reaching task

“…stopwatch) and can indicate learning even in clinical populations (Felix et al, 2012). Moreover, upper extremity movement time is related to the degree of white matter lesion (Wright et al, 2008), dementia (Bramell-Risberg et al, 2010; Yan and Dick, 2006), and lower extremity movement time (Michmizos et al, 2014). Its sensitivity and ease of measurability are two of many reasons why movement time is a key variable to measure, both experimentally and clinically.…”