A modular, deep learning-based holistic intent sensing system tested with Parkinson's disease patients and controls.

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.

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Copyright © 2023 Russell, Inches, Carroll and Bergmann.

Déclaration de conflit d'intérêts

JI receives salary from University Hospitals Plymouth NHS Trust. CBC has received honoraria from Bial, GKC, AbbVie, Kyowa Kirin, Lundbeck, Britannia, and Medscape. She has received service grants from AbbVie and Bial, and research grants from Parkinson’s UK, Cure Parkinson’s, National Institute for Health Research, and the Edmond J Safra Foundation. She receives salary from Newcastle University, University of Plymouth, University Hospitals Plymouth National Health Service Trust, Parkinson’s UK and National Institute of Health and Care Research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.