Pen-point Trajectory Analysis During Trail Making Test Based on a Time Base Generator Model

Sakai, Hiroto; Furui, Akira; Hama, Seiji; Yanagawa, Akiko; Kubo, Koki; Morisako, Yutaro; Orino, Yuki; Hamai, Maho; Fujita, Kasumi; Mizuguchi, Tomohiko; Kandori, Akihiko; Tsuji, Takao

doi:10.1109/embc46164.2021.9629991

Cited by 8 publications

(29 citation statements)

References 14 publications

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“…Suppose the tasks have now been segmented and the clinician would like to analyze the functional primitive kinematics of how individuals reach for and grasp the bottle of vinegar (e.g., some kinematic analyses require primitive segmentation ( 97 , 105 , 106 )). This reaching motion consists of multiple functional primitives (see Table 3 ) and would need to be segmented.…”

Section: Movement Segmentationmentioning

confidence: 99%

“…Again, one option would be to manually segment the primitives ( 48 ). However, there are algorithms that focus specifically on automating functional primitive segmentation (e.g., ( 26 , 106 )). Given the distinction between the algorithms for segmenting functional movements and primitives, one section is dedicated to each in this review.…”

Section: Movement Segmentationmentioning

confidence: 99%

“…To remedy this, ( 26 ) proposed a movement primitive segmentation approach that uses distance from the object and event recordings to segment reaching from object manipulation. This method has since been used to segment the reach and dwell primitives of pen point trajectories during the Trail Making Test to assess cognitive function ( 106 ).…”

Section: Movement Segmentationmentioning

confidence: 99%

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Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

Jackson

Duric

Engdahl

et al. 2023

Front. Rehabil. Sci.

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The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.

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Section: Movement Segmentationmentioning

confidence: 99%

Section: Movement Segmentationmentioning

confidence: 99%

Section: Movement Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

Jackson

Duric

Engdahl

et al. 2023

Front. Rehabil. Sci.

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“…A more detailed analysis of the Γ -function is provided by the TBG (Time base Generator) model ( Tsuji et al, 2002 ) that allows a parametrization of the function, in order to describe small modifications of the symmetry of the bell-shaped velocity profile that are consistently associated with any individual ( Kittaka et al, 2020 ). This method of analysis is also applicable in the clinical field by using it for the quantitative evaluation of the Trail Making test ( Sakai et al, 2021 ): this is a neuropsychological test which is widely used to assess the cognitive function of patients with motor-cognitive impairments, as in the case of stroke.…”

Section: The Generalized Information Processing Systems Approachmentioning

confidence: 99%

A Vexing Question in Motor Control: The Degrees of Freedom Problem

Morasso

2022

Front. Bioeng. Biotechnol.

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The human “marionette” is extremely complex and multi-articulated: anatomical redundancy (in terms of Degrees of Freedom: DoFs), kinematic redundancy (movements can have different trajectories, velocities, and accelerations and yet achieve the same goal, according to the principle of Motor Equivalence), and neurophysiological redundancy (many more muscles than DoFs and multiple motor units for each muscle). Although it is quite obvious that such abundance is not noxious at all because, in contrast, it is instrumental for motor learning, allowing the nervous system to “explore” the space of feasible actions before settling on an elegant and possibly optimal solution, the crucial question then boils down to figure out how the nervous system “chooses/selects/recruits/modulates” task-dependent subsets of countless assemblies of DoFs as functional motor synergies. Despite this daunting conceptual riddle, human purposive behavior in daily life activities is a proof of concept that solutions can be found easily and quickly by the embodied brain of the human cognitive agent. The point of view suggested in this essay is to frame the question above in the old-fashioned but still seminal observation by Marr and Poggio that cognitive agents should be regarded as Generalized Information Processing Systems (GIPS) and should be investigated according to three nearly independent but complementary levels of analysis: 1) the computational level, 2) the algorithmic level, and 3) the implementation level. In this framework, we attempt to discriminate as well as aggregate the different hypotheses and solutions proposed so far: the optimal control hypothesis, the muscle synergy hypothesis, the equilibrium point hypothesis, or the uncontrolled manifold hypothesis, to mention the most popular ones. The proposed GIPS follows the strategy of factoring out shaping and timing by adopting a force-field based approach (the Passive Motion Paradigm) that is inspired by the Equilibrium Point Hypothesis, extended in such a way to represent covert as well overt actions. In particular, it is shown how this approach can explain spatio-temporal invariances and, at the same time, solve the Degrees of Freedom Problem.

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“…Assessments of functional upper extremity (UE) movement quality are commonly used in the clinic and provide useful treatment outcome measures. These assessments typically require an individual to perform standardized functional tasks, such as moving small blocks over a partition under time constraint [1] and writing and drawing [2] , [3] . Functional assessment measures also include self-reports, such as the Quick Disabilities of Arm, Shoulder, and Hand (QuickDASH) [4] .…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

Jackson

Duric

Engdahl

et al. 2024

IEEE J. Transl. Eng. Health Med.

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There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

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Pen-point Trajectory Analysis During Trail Making Test Based on a Time Base Generator Model

Cited by 8 publications

References 14 publications

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

A Vexing Question in Motor Control: The Degrees of Freedom Problem

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

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