# iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters

arXiv: https://arxiv.org/abs/2401.15473 · HTML: https://mlsanity.com/paper/2401.15473/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 118 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [BiosecurID: a multimodal biometric database](https://mlsanity.com/paper/2111.03472/) (2021)

## Built on by (most cited first, 7 of 7)

- [Writing Order Recovery in Complex and Long Static Handwriting](https://mlsanity.com/paper/2406.03194/) (2024)
- [A Machine Learning Approach to Analyze the Effects of Alzheimer's Disease on Handwriting through Lognormal Features](https://mlsanity.com/paper/2405.16959/) (2024)
- [Synthesis of 3D on-air signatures with the Sigma-Lognormal model](https://mlsanity.com/paper/2401.16329/) (2024)
- [Extending the kinematic theory of rapid movements with new primitives](https://mlsanity.com/paper/2401.16519/) (2024)
- [Graphomotor and Handwriting Disabilities Rating Scale (GHDRS):towards complex and objective assessment](https://mlsanity.com/paper/2405.17886/) (2024)
- [From operculum and body tail movements to different coupling of physical activity and respiratory frequency in farmed gilthead sea bream and European sea bass. Insights on aquaculture biosensing](https://mlsanity.com/paper/2406.03859/) (2024)
- [Telling Human and Machine Handwriting Apart](https://mlsanity.com/paper/2601.11700/) (2026)

## Abstract (arXiv)

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.