» Articles » PMID: 38020624

A Modular, Deep Learning-based Holistic Intent Sensing System Tested with Parkinson's Disease Patients and Controls

Overview
Journal Front Neurol
Specialty Neurology
Date 2023 Nov 29
PMID 38020624
Authors
Affiliations
Soon will be listed here.
Abstract

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.

References
1.
Bergmann J, Chandaria V, McGregor A . Wearable and implantable sensors: the patient's perspective. Sensors (Basel). 2013; 12(12):16695-709. PMC: 3571806. DOI: 10.3390/s121216695. View

2.
Gantenbein J, Dittli J, Meyer J, Gassert R, Lambercy O . Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation. Front Neurorobot. 2022; 16:815693. PMC: 8900616. DOI: 10.3389/fnbot.2022.815693. View

3.
Von Rosen P, Hagstromer M, Franzen E, Leavy B . Physical activity profiles in Parkinson's disease. BMC Neurol. 2021; 21(1):71. PMC: 7881685. DOI: 10.1186/s12883-021-02101-2. View

4.
Kim J, Lee G, Heimgartner R, Arumukhom Revi D, Karavas N, Nathanson D . Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science. 2019; 365(6454):668-672. DOI: 10.1126/science.aav7536. View

5.
Uddin M, Soylu A . Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Sci Rep. 2021; 11(1):16455. PMC: 8361103. DOI: 10.1038/s41598-021-95947-y. View