A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls, by Joseph Russell, Jemma Inches, Camille B. Carroll and Jeroen H.M. Bergmann - 2023

A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls, by Joseph Russell, Jemma Inches, Camille B. Carroll and Jeroen H.M. Bergmann, narrated in full by Joseph Russell, as published on 1st November 2023 in Frontiers in Neurology, 14:1260445. Full article available at: https://doi.org/10.3389/fneur.2023.1260445 This video is part of an initiative by SDU's Biomedical Engineering Centre to improve the accessibility of scientific papers with human-narrated article readings. The text is read out in full in the same manner as a non-fiction unabridged audiobook, with the article displayed page-by-page on-screen for those wishing to see figures, equations and references. 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. © 2023 by the authors.