On Computer Mouse Pointing Model Online Identification and Endpoint Prediction

Abstract

This paper proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm's implementation.

Data-efficient human walking speed intent identification

“…Autonomous mobile robots may navigate around moving humans (Aoude et al, 2013 ), whereas robotic lower-limb prostheses (Wentink et al, 2013 ; Yuan et al, 2022 ) and exoskeletons (Jung et al, 2012 ; Zhang et al, 2022b ) support stable, efficient gait in coordination with the user. These applications all require the ability to reason about what the human aims to accomplish (Malmi, 2013 ; Abbink et al, 2018 ; Chauhan et al, 2019 ; Khalin et al, 2021 ; Kalinowska et al, 2023 ). Robots assisting locomotion rely on a variety of sensors to glean information related to the human’s intentions.…”