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dc.contributor.authorRussell, J
dc.contributor.authorInches, J
dc.contributor.authorCarroll, CB
dc.contributor.authorBergmann, JHM
dc.date.accessioned2023-12-18T10:56:30Z
dc.date.available2023-12-18T10:56:30Z
dc.date.issued2023-11-01
dc.identifier.issn1664-2295
dc.identifier.issn1664-2295
dc.identifier.otherARTN 1260445
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/21800
dc.description.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.

dc.format.extent1260445-
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.publisherFrontiers Media SA
dc.subjectParkinson's disease
dc.subjectwearable sensors
dc.subjectintent sensing
dc.subjectdeep learning
dc.subjectassistive medical devices
dc.titleA modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/38020624
plymouth.volume14
plymouth.publisher-urlhttp://dx.doi.org/10.3389/fneur.2023.1260445
plymouth.publication-statusPublished online
plymouth.journalFrontiers in Neurology
dc.identifier.doi10.3389/fneur.2023.1260445
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Research Groups
plymouth.organisational-group|Plymouth|Faculty of Health
plymouth.organisational-group|Plymouth|Research Groups|Institute of Translational and Stratified Medicine (ITSMED)
plymouth.organisational-group|Plymouth|Research Groups|Institute of Translational and Stratified Medicine (ITSMED)|CCT&PS
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group|Plymouth|Faculty of Health|Peninsula Medical School
plymouth.organisational-group|Plymouth|Research Groups|FoH - Community and Primary Care
plymouth.organisational-group|Plymouth|Research Groups|FoH - Applied Parkinson's Research
plymouth.organisational-group|Plymouth|Research Groups|Plymouth Institute of Health and Care Research (PIHR)
plymouth.organisational-group|Plymouth|REF 2028 Researchers by UoA
plymouth.organisational-group|Plymouth|REF 2028 Researchers by UoA|UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
dc.publisher.placeSwitzerland
dcterms.dateAccepted2023-10-05
dc.date.updated2023-12-18T10:56:29Z
dc.rights.embargodate2023-12-19
dc.identifier.eissn1664-2295
rioxxterms.versionofrecord10.3389/fneur.2023.1260445


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