Search research articles

Search research articles

Related Experiment Videos

A computational procedure for movement analysis in handwriting

C Marquardt¹, N Mai

¹EKN Entwicklungsgruppe Klinische Neuropsychologie, Städtisches Krankenhaus München-Bogenhausen, Munich, Germany.

Journal of Neuroscience Methods

|April 1, 1994

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Kinaesthetic motor imagery in writer's cramp dystonia reveals writing specific abnormalities in the occipital lobe.

Neuroscience·2025

Same author

[Rare complication of a parastomal hernia in a patient with an ileal conduit].

Chirurgie (Heidelberg, Germany)·2024

Same author

Variation in training requirements within general surgery: comparison of 23 countries.

BJS open·2021

Same author

The effect of soil type on the extraction of insensitive high explosive constituents using four conventional methods.

The Science of the total environment·2019

Same author

Investigation into the environmental fate of the combined Insensitive High Explosive constituents 2,4-dinitroanisole (DNAN), 1-nitroguanidine (NQ) and nitrotriazolone (NTO) in soil.

The Science of the total environment·2018

Same author

Release of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) from polymer-bonded explosives (PBXN-109) into water by artificial weathering.

Chemosphere·2016

Same journal

Pupil-DLC: an open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.

Journal of neuroscience methods·2026

Same journal

Time as the language of Behavior: events, sequences, patterns and meanings.

Journal of neuroscience methods·2026

Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026

Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026

Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026

Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Manufacturer specifications do not guarantee handwriting movement data accuracy. A novel kernel-based smoothing method effectively reduces noise in velocity and acceleration signals from digitizing tablets.

Area of Science:

Biomedical Engineering
Signal Processing
Human-Computer Interaction

Background:

Digitizing tablets capture handwriting movement data, but manufacturer specifications often misrepresent positional data accuracy.
Errors in positional data are amplified during the calculation of time derivatives (velocity, acceleration), necessitating robust data smoothing techniques.

Purpose of the Study:

To introduce and evaluate a new method for smoothing and differentiating noisy handwriting movement data.
To compare the efficacy of the proposed method against traditional Butterworth and Finite Impulse Response (FIR) filters.

Main Methods:

Non-parametric estimation of regression functions using kernel estimates for data smoothing and differentiation.
Assessment of the method's efficiency using simulation data and comparison with Butterworth and FIR filters.

Related Experiment Videos

Main Results:

Kernel estimates demonstrated a slightly increased bias but significantly reduced residual variance in velocity and acceleration signals.
The proposed kernel-based method exhibited superior smoothing compared to Butterworth filters and comparable performance to FIR filters when transition bands were optimized.

Conclusions:

Kernel estimates offer an effective and computationally efficient approach for smoothing and differentiating noisy handwriting data.
This method provides a more reliable way to analyze handwriting dynamics, overcoming limitations of existing techniques and manufacturer specifications.