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Backgound: Metrics for movement smoothness include the number of zero-crossings on the acceleration profile (N0C), the log dimensionless jerk (LDLJ), the normalized averaged rectified jerk (NARJ) and the spectral arc length (SPARC). Sensitivity to the handedness and movement type of these four metrics was compared and correlations with other kinematic parameters were explored in healthy subjects.
Methods: Thirty-two healthy participants underwent 3D upper limb motion analysis during two sets of pointing movements on each side. They performed forward- and backward-pointing movements at a self-selected speed to a target located ahead at shoulder height and at 90% arm length, with and without a three-second pause between forward and backward movements. Kinematics were collected, and smoothness metrics were computed.
Results: LDLJ, NARJ and N0C found backward movements to be smoother, while SPARC found the opposite. Inter- and intra-subject coefficients of variation were lowest for SPARC. LDLJ, NARJ and N0C were correlated with each other and with movement time, unlike SPARC.
Conclusion: There are major differences between smoothness metrics measured in the temporal domain (N0C, LDLJ, NARJ), which depend on movement time, and those measured in the frequency domain, the SPARC, which gave results opposite to the other metrics when comparing backward and forward movements.
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http://dx.doi.org/10.3390/s23031158 | DOI Listing |
J Vis
September 2025
Vrije Universiteit Amsterdam, Amsterdam Movement Sciences and Institute Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Amsterdam, Netherlands.
Eye tracking has the potential to be used as a meaningful measure of the consequences of vision impairment (VI), yet a comprehensive test battery is lacking. In this study, we sought to evaluate the feasibility and validity of a test battery of eye movements as a tool to measure visual performance in individuals with VI. A test battery including fixation stability, smooth pursuit, saccades, free viewing, and visual search was administered to 46 athletes with VI and 10 control participants.
View Article and Find Full Text PDFJ Am Coll Surg
September 2025
Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL.
Background: The NSQIP Pediatric Semi-annual report (NSQIP Ped SAR) provides hospitals with risk-adjusted benchmarked results for comparative performance based on 1 year of data. These data are 6 to 18 months old due to requirements for data processing and modeling and this delay potentially limits its usefulness for hospital surgical quality improvement efforts. A timelier reporting mechanism is needed.
View Article and Find Full Text PDFbioRxiv
September 2025
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
High-throughput spatial transcriptomics (ST) now profiles hundreds of thousands of cells or locations per section, creating computational bottlenecks for routine analysis. Sketching, or intelligent sub-sampling, addresses scale by selecting small, representative subsets. While effective for scRNA-seq data, existing sketching methods, which optimize coverage in expression space but ignore physical location, can introduce spatial bias when applied to ST data.
View Article and Find Full Text PDFGait Posture
September 2025
School of Health Sciences, University of East Anglia, UK. Electronic address:
Background: International consensus recommends use of kinematic metrics of movement during standardized functional tasks after stroke to ascertain whether rehabilitation is driving behavioral restitution or compensation. Quality of human movement can be characterized by fluency metrics including smoothness and hesitation. Before using these metrics in stroke rehabilitation it is important to find whether 'reference values', from healthy adults, are repeatable.
View Article and Find Full Text PDFGenes Genomics
September 2025
Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea.
Background: Muscle-invasive bladder cancer (MIBC) is a clinically aggressive and heterogeneous disease with variable treatment responses. Transcriptome-based classifications, such as the Chemoresistance-Motility (CrM) signature, are valuable for understanding therapeutic resistance, but their clinical use is often hindered by high cost and tissue requirements. This study explores an alternative, scalable approach using deep learning analysis of whole slide images (WSIs).
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