Reaching tasks are considered well-executed if they appear "smooth," a quality that is typically quantified by its opposite, jerk, the rate of change of acceleration. While jerk is a theoretically sound measure, its application to spastic individuals sometimes yields counter-intuitive results, and does not reveal motor impairment across the workspace. To more generally quantify spontaneous accelerative transients (SATs) within a movement, a pseudo-wavelet transform was devised that iteratively compared angular trajectories to a series of straight-line approximants. Cumulative linear fit errors were expressed in terms of flexion angle, yielding an SAT map of the entire motion. To compare SAT maps with traditional smoothness measures, two scalar indices were extracted from them: residual excursion deviation (RED), representing the integral over Deltatheta and the ratio of peak error to mean error (PEME) on the map. Fifteen subjects, including five subjects with chronic stroke performed elbow flexions throughout their entire ranges of motion, Deltatheta, at a comfortable pace with their arms supported in the transverse plane. Maps revealed that stroke subjects were significantly less coordinated than controls, as measured both by RED: 8.0+/-2.9 x 10(-3) versus 3.1+/-0.8 x 10(-3) and PEME: 6.6+/-0.9 versus 12.1+/-1.9, both P<0.001. Comparable jerk metrics, including integrated average jerk, did not report a significant performance deficit at the P<0.05 level. Map metrics for all subjects were independent of average velocity (correlation with theta : rho0.31), but jerk-based metrics for stroke subjects were spuriously co-variant with velocity rho=0.85, which may relate to the significantly higher mean arrest period ratio in stroke subjects (0.26+/-0.19 versus 0.09+/-0.08, P<0.001). We conclude that SAT maps provide reliable information on regional movement impairments at a wide range of proficiency levels.
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http://dx.doi.org/10.1016/j.jbiomech.2008.10.015 | DOI Listing |
JMIR Med Inform
January 2025
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.
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Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFJ Integr Neurosci
January 2025
Department of Physical Therapy, Hangzhou Geriatric Hospital, 310022 Hangzhou, Zhejiang, China.
Background: Observation, execution, and imitation of target actions based on mirror neuron network (MNN) have become common physiotherapy strategies. Electrical stimulation (ES) is a common intervention to improve muscle strength and motor control in rehabilitation treatments. It is possible to enhance MNN's activation by combining motor execution (ME) and motor imitation (MI) with ES simultaneously.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
View Article and Find Full Text PDFChildren (Basel)
December 2024
Department of Pediatrics, Division of Neonatology, University of Virginia, Charlottesville, VA 22908, USA.
Background/objectives: Motor deficits following neonatal brain injury, from cerebral palsy to subtle deficits in motor planning, are common yet underreported. Rodent models of motor deficits in neonatal hypoxia-ischemia (HI) allow improved understanding of the underlying mechanisms and neuroprotective strategies. Our goal was to test motor performance and learning in a mouse model of neonatal HI.
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