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Lookup NU author(s): Professor Camille CarrollORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2023 Russell, Inches, Carroll and Bergmann.People living with mobility-limiting conditions such as Parkinson’s disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson’s disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices.
Author(s): Russell J, Inches J, Carroll CB, Bergmann JHM
Publication type: Article
Publication status: Published
Journal: Frontiers in Neurology
Year: 2023
Volume: 14
Online publication date: 01/11/2023
Acceptance date: 05/10/2023
Date deposited: 30/01/2024
ISSN (electronic): 1664-2295
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/fneur.2023.1260445
DOI: 10.3389/fneur.2023.1260445
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