High curvature and jerk analyses of arm ataxia

Abstract

We investigated high curvature analysis (HCA) and integrated absolute jerk (IAJ) for differrentiating healthy and cerebellopathy (CB) patients performing pointing tasks. Seventeen CB patients and seventeen healthy controls were required to move a pointer at their preferred pace between two 50.8 cm laterally spaced targets while standing with theirarm extended in front of their body. HCA was used to quantify the frequency of sharp turns in the horizontal-plane (anterior-posterior and medio-lateral) velocity trajectory of the hand-held pointer. IAJ was assesssed by integration of absolute jerk (second time derivative of velocity) time histories in the anterior-posterior and medio-lateral directions. HCA scores and IAJ scores were then compared between CB patients and healthy controls; for both analyses, higher scores indicateless smooth movements. We hypothesized that CB patients would have less smooth movement trajectories than healthy controls due to upper extremity ataxia asssociated with cerebellar disease and degeneration. We found that CB patients had higher HCA scores than healthy controls (P = 0.014). Although CB patients had higher IAJ scores in both anterior-posterior (P = 0.060)and medio-lateral (P = 0.231) directions compared to the healthy controls, the differences were not significant. The difference in sensitivity between the HCA andthe IAJ analysis might be explained by primitive neural activation commands, ubiquitous though only evident with some cerebellar dysfunctions, which produce submovements which are themselves minimal jerk curves. We conclude that HCA may be a useful tool for quantifying upper extremity ataxia in CB patients performing a repeated pointing task.

Spatial resolution of spontaneous accelerations in reaching tasks

“…Traditional time-domain metrics of smoothness such as jerk are convenient for comparative purposes, but scalar quantities based on temporal information are susceptible to artefact associated with signal processing techniques (Hsiang et al, 1999;Dabroom and Khalil, 1999) and are opaque to the locality of performance deficits in space. Furthermore, jerk-based assessments occasionally report insignificant performance deficits in impaired cohorts, or even increased jerk with rehabilitation, contradicting other smoothness measures (Goldvasser et al, 2001;Rohrer et al, 2002).…”

Insight into Goal-Directed Movements: Beyond Fitts’ Law

“…total path length divided by total time 1 Counter-productive submovements: Number of times the coincident error is negative with a magnitude larger than the submovement's starting distance 1 [9] Deceleration time: Time interval during which the pointer was decelerating 1 [18] Distance to peak speed: Distance traveled from movement onset to the moment peak speed is reached 2 Error magnitude: Distance from the position of the target miss (button click) to the target edge 3 Final positioning time: Interval from target entry until the end of the trial 3 [2] Goal distance correction phase: Distance to the target at the start of the correction phase 3 High curvature occurrence: Number of times the angle between 3 sample points is less than 80 deg 1 [6] Length offset: Difference between the length of the ballistic phase and 'distance to target' at the beginning of the ballistic phase 2 [8] Max percent overshoot: largest percent deviation from the target once the pointer passes the target 2 [3] Movement direction change: Number of times the tangent to the path gets parallel to the task axis 1 [14] Movement error: Mean of absolute distances of the path from the task axis 1 [14] Movement offset: Overall mean distances of the path from the task axis 1 [14] Movement time: time interval from movement onset to movement offset 4 [16] Movement variability: Extent to which the path lies in a straight line parallel to the task axis 1 [14] Orthogonal direction change: Number of times the tangent to the path becomes perpendicular to the task axis 1 [14] Overshoot: the frequency and duration (time interval from the moment the pointer passes the target edge to movement offset) of overshoots 2 [9,3] Path length: Length of the path in mm 1 Path length efficiency: Ratio between the shortest path and the traveled path 1 [11] Pauses: Number of pauses (>0ms; >100ms; >250ms) [11] and mean duration of the pauses 1 [9] Peak acceleration: Maximum acceleration reached during the overall movement 2 [7] Peak deceleration: Maximum deceleration reached during the overall movement 2 [7] Peak speed: Maximum speed reached during the movement 2 [11] Peak time: time at which maximum overshoot is reached 2 [3] Perpendicular error: Distance between the endpoint of the ballistic phase and the task axis, measured in the direction normal to the task axis 2 …”

Jerk Analysis of the Spine by Means of a Parallel Manipulator

“…Gielen et al (1997) noted that the charactecteristic pattern of cerebellar ataxia, related with the jerk and submovements, is contained in the trajectory of the hand during repeated arm movements. Goldvasser et al (2001) investigated high curvature analysis and integrated absolute jerk for differentiating healthy and cerebellopathy patients performing pointing tasks. In order to understand how the central nervous system controls the kinematics of rapid finger and handmovement, Novak et al (2000) proposed an objective algorithm to identify overlapping submovements, detecting appreciable inflections in the acceleration traces which are related, evidently, with the jerk.…”