Sensorimotor Control of Tracking Movements at Various Speeds for Stroke Patients as Well as Age-Matched and Young Healthy Subjects

Abstract

There are aging- and stroke-induced changes on sensorimotor control in daily activities, but their mechanisms have not been well investigated. This study explored speed-, aging-, and stroke-induced changes on sensorimotor control. Eleven stroke patients (affected sides and unaffected sides) and 20 control subjects (10 young and 10 age-matched individuals) were enrolled to perform elbow tracking tasks using sinusoidal trajectories, which included 6 target speeds (15.7, 31.4, 47.1, 62.8, 78.5, and 94.2 deg/s). The actual elbow angle was recorded and displayed on a screen as visual feedback, and three indicators, the root mean square error (RMSE), normalized integrated jerk (NIJ) and integral of the power spectrum density of normalized speed (IPNS), were used to investigate the strategy of sensorimotor control. Both NIJ and IPNS had significant differences among the four groups (P<0.01), and the values were ranked in the following order: young controls < age-matched controls <unaffected sides of stroke patients <affected sides of stroke patients, which could be explained by the stroke- and aging-induced increase in reliance on feedback control. The RMSE increased with the increase in the target speed and the NIJ and IPNS initially declined and then remained steady for all four groups, which indicated a shift from feedback to feedforward control as the target speed increased. The feedback-feedforward trade-off induced by stroke, aging and speed might be explained by a change in the transmission delay and neuromotor noise. The findings in this study improve our understanding of the mechanism underlying the sensorimotor control and neurological changes caused by stroke and aging.

Development of a quantitative evaluation system for visuo-motor control in three-dimensional virtual reality space

“…In our previous studies 38 , 39 , to ensure patients with cerebellar ataxia can perform stable tracking movements in two-dimensional space, we set the target size five times greater than the tracer size. On the other hand, in the studies on target tracking strategies regarding feedback and/or feedforward control in one-dimensional or two-dimensional spaces 36 , 40 , 41 , the target size was set to the same as the tracer size. In this study, we also aimed to quantitatively analyze the target tracking strategies using the proposed system for visuo-motor control.…”

“…( Artif Life Robotics ) 2009 40 6 mm 6 mm Ao et al . ( Plos One ) 2015 41 1 cm × 3 cm (slider) 1 cm × 3 cm (slider) Lee et al . ( Cerebellum ) 2012 38 ( PLoS One ) 2015 39 10 mm 2 mm …”

On the analysis of movement smoothness

“…On the other hand, it must be noted that the available smoothness measures have primarily focused on discrete arm movements, such as point-to-point reaching and circle drawing [ 17 ]. A few studies have also investigated the smoothness of the kinematics of other body segments, such as the head [ 18 ], jaw [ 19 ], elbow [ 20 , 21 ], forearm rotation [ 22 ], wrist [ 23 ], lower-extremity [ 24 – 27 ] etc. Additionally, some studies have also investigated the smoothness of rhythmic movements [ 28 ], including walking [ 24 – 26 , 29 ], back-and-forth elbow flexion/extension [ 21 ], and rhythmic object manipulation [ 30 ].…”

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

“…Moreover, with digitized motion signals, we can easily calculate parameters defined in the time and frequency domain. For instance, in the time domain, the RMSE (Root Mean Squared Error) [52,53] can be calculated, whereas in the frequency domain, we can compute the mean amplitude related to certain frequency band, or the parameter IPNS (Integral of the Power spectrum density of Normalized Speed) [53]. The proper use of these parameters requires the establishment of ranges in time or space in which the parameters should be calculated.…”

“…To evaluate learning quality, we used three different parameters. The first factor was based on the RMSE parameter [53] and estimated the accuracy of the motion learned by the person [1,52]. We calculated the RMSE from each preprocessed one-dimensional signal S i = (s 1 i , s 2 i , .…”