Spatial resolution of spontaneous accelerations in reaching tasks

Wininger, Michael; Kim, Namhun; Craelius, William

doi:10.1016/j.jbiomech.2008.10.015

Cited by 29 publications

(29 citation statements)

References 22 publications

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“…This has been demonstrated repeatedly to be true for healthy individuals (Flash and Hogan, 1985;Plamondon, 1991;Harris and Wolpert, 1998;Pattacini et al, 2010). More relevant to rehabilitation robotics, this framework has been shown to apply even in severely impaired individuals: movement onset and cessation is from rest or at low speed if transitioning in a movement re-direction; velocity profiles are typically single or multiple peaked (Trombly, 1993;Cirstea and Levin, 2000;Rohrer et al, 2002;Wininger et al, 2009). To wit, this principle is embedded in robot-based assistive technology (Patton and Mussa-Ivaldi, 2004;Kahn et al, 2006;Velliste et al, 2008;Mazzoleni et al, 2014).…”

Section: Key Assumptionmentioning

confidence: 84%

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

et al. 2017

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Point-to-point exercising of the upper-limb, as elicited through the presentation of visual targets on a computer screen, is a ubiquitous paradigm in the robot-assisted rehabilitation of motor-impaired individuals. Kinematic data collected from the robot's sensors can be used to assess motor function; these data allow objective quantification of motor performance, an approach that shows promise both for guiding therapy and documenting patient progress. It is imperative that these datasets be fully understood and that tools be continually developed to support analysis and proper interpretation of robot-generated data. It is our experience that data collected from kinematic robots and partitioned according to target achievement may be prone to errors in analysis and interpretation because the movements of highly spastic individuals rarely stop within the target. Here, we propose that it is preferable to partition serial movement data based on local minima in velocity rather than target achievement; this design reflects the convention that movement epochs start and end at low or zero velocity, an assumption that is prevalent even in severely impaired individuals. Using a commercially available robot (MIT-Manus, Interactive Motion Technologies), we recorded movements from 16 moderate to severely impaired chronic stroke patients. Data partitioned according to target presentation typically interrupted movements in mid-motion: velocity at file start was 32.6 ± 26.4% of the overall velocity range. By re-apportioning, we obtained velocity at file start of 7.4 ± 9.5% of total range. Across 3,200 movements, 12.4 ± 10.4% of data points were re-allocated on average. Thus, our routine is capable of re-partitioning to more accurately reflect observed behavior. Our study is thus the first to identify and propose a solution to the problem of high relevance to the community of robot-aided rehabilitation specialists, i.e., suboptimal partitioning according to target achievement. Through the algorithm described in this paper, we were able to re-partition the data so that movement epochs were properly demarcated at velocity minima, thus adhering to the fundamental assumptions of human motor behavior and facilitating analysis of patient performance on a per-movement basis.

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Section: Key Assumptionmentioning

confidence: 84%

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

et al. 2017

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“…While recognize that some will consider clustering-by-trajectory as a more direct approach, we believe clustering within the feature space to be a reasonable surrogate for trajectory, and caution that comparison of trajectories is a complex enterprise: patients with neuromotor dysfunction can move slowly or rapidly, and sometimes both slowly and rapidly in the same data sample. In moderately-impaired populations, movement arrest can be particularly prevalent (Beppu et al, 1984;Rohrer et al, 2002), and can confound attempts to obtain meaningful waveform correlations (Wininger et al, 2009). …”

Section: Discussion Study Validitymentioning

confidence: 99%

“…In addition to basic movement parameters of amplitude, duration, average-and peak velocity, average-and peak acceleration, we calculated four descriptors related to movement smoothness including the number of peaks in the velocity trace (Fetters and Todd, 1987;Michaelsen et al, 2006;Thelen et al, 1993), the tent metric i.e., the ratio of the area under the convex hull fitted to the velocity trace versus the area under the velocity trace itself (Rohrer et al, 2002), and two formulations of jerk (Hogan and Sternad, 2009;Rohrer et al, 2002). Where jerk-based metrics have been shown to be especially sensitive to basic movement parameters (Wininger et al, 2009(Wininger et al, , 2012, we tested one formulation witha widely analyzed behavior…”

Section: Feature Extractionmentioning

confidence: 99%

“…Because these features are expected to be highly collinear (Wininger et al, 2009(Wininger et al, , 2012, feature set dimensionality was reduced via Principal Components Analysis (PCA) over the entire dataset, i.e., all participants all days. A minimum threshold was established a priori at 80%: the minimum number of PCs accounting for greater than 80% of the variance would be retained for analysis.…”

Section: Feature Extractionmentioning

confidence: 99%

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Clustering of Directions Improves Goodness of Fit in Kinematic Data Collected in the Transverse Plane During Robot-Assisted Rehabilitation of Stroke Patients

Hartigan

Peduzzi

et al. 2018

Front. Robot. AI

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The kinematic character of hand trajectory in reaching tasks varies by movement direction. Often, direction is not included as a factor in the analysis of data collected during multidirectional reach tasks; consequently, this directionally insensitive model (DI) may be prone to type-II error due to unexplained variance. On the other hand, directionally specific models (DS) that account separately for each movement direction, may reduce statistical power by increasing the amount of data groupings. We propose a clustered-bysimilarity (CS) in which movement directions with similar kinematic features are grouped together, maximizing model fit by decreasing unexplained variance while also decreasing uninformative sub-groupings. We tested model quality in measuring change over time in 10 kinematic features extracted from 72 chronic stroke patients participating in the VA-ROBOTICS trial, performing a targeted reaching task over 16 movement directions (8 targets, back-and forth from center) in the horizontal plane. Across 49 participants surviving a quality control sieve, 4.3 ± 1.1 (min: 3; max: 7) clusters were found among the 16 movement directions; clusters varied between participants. Among 49 participants, and averaged across 10 features, the better-fitting model for predicting change in features was found to be CS assessed by the Akaike Information criterion (61.6 ± 7.3%), versus DS (31.0 ± 7.8%) and DI (7.1 ± 7.1%). Confirmatory analysis via Extra Sum of Squares F-test showed the DS and CS models out-performed the DI model in head-to-head (pairwise) comparison in >85% of all specimens. Thus, we find overwhelming evidence that it is necessary to adjust for direction in the models of multi-directional movements, and that clustering kinematic data by feature similarly may yield the optimal configuration for this co-variate.

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“…Hogan and Sterned (2009) indicated that the reason why many studies based on the fundamental principle of jerk (Goldvasser, McGibbon, & Krebs, 2001;Wininger, Kim, & Craelius, 2009) showed inconsistent results was because of the sensory-motor dysfunction that each individual has. This theory is based on an assumption that each individual has a varying degree of control over his or her own movement, and because many variables are involved in an individual's ability to control motion and movement, it is difficult to quantify and to analyze jerk collectively or with simple units, or specific variables.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis between Dance Experience and Smoothness of Dance Movement by Using Three Jerk-Based Quantitative Methods

Park¹

2016

Korean Journal of Sport Biomechanics

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Spatial resolution of spontaneous accelerations in reaching tasks

Cited by 29 publications

References 22 publications

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

Clustering of Directions Improves Goodness of Fit in Kinematic Data Collected in the Transverse Plane During Robot-Assisted Rehabilitation of Stroke Patients

Correlation Analysis between Dance Experience and Smoothness of Dance Movement by Using Three Jerk-Based Quantitative Methods

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