Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/83191 |
Resumo: | Freezing of gait (FoG) is one of the most disabling motor symptoms in Parkinson’s disease, which is described as a symptom where walking is interrupted by a brief, episodic absence, or marked reduction, of forward progression despite the intention to continue walking. Although FoG causes are multifaceted, they often occur in response of environment triggers, as turnings and passing through narrow spaces such as a doorway. This symptom appears to be overcome using external sensory cues. The recognition of such environments has consequently become a pertinent issue for PD-affected community. This study aimed to implement a real-time DL-based door detection model to be integrated into a wearable biofeedback device for delivering on-demand proprioceptive cues. It was used transfer-learning concepts to train a MobileNet-SSD in TF environment. The model was then integrated in a RPi being converted to a faster and lighter computing power model using TensorFlow Lite settings. Model performance showed a considerable precision of 97,2%, recall of 78,9% and a good F1-score of 0,869. In real-time testing with the wearable device, DL-model showed to be temporally efficient (~2.87 fps) to detect with accuracy doors over real-life scenarios. Future work will include the integration of sensory cues with the developed model in the wearable biofeedback device aiming to validate the final solution with end-users. |
id |
RCAP_8dbfd2a6d0cddf24085c6b9b9ea4118b |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/83191 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s diseaseObject detectionDeep-learningRPiParkinson’s diseaseEngenharia e Tecnologia::Engenharia MédicaScience & TechnologySaúde de qualidadeFreezing of gait (FoG) is one of the most disabling motor symptoms in Parkinson’s disease, which is described as a symptom where walking is interrupted by a brief, episodic absence, or marked reduction, of forward progression despite the intention to continue walking. Although FoG causes are multifaceted, they often occur in response of environment triggers, as turnings and passing through narrow spaces such as a doorway. This symptom appears to be overcome using external sensory cues. The recognition of such environments has consequently become a pertinent issue for PD-affected community. This study aimed to implement a real-time DL-based door detection model to be integrated into a wearable biofeedback device for delivering on-demand proprioceptive cues. It was used transfer-learning concepts to train a MobileNet-SSD in TF environment. The model was then integrated in a RPi being converted to a faster and lighter computing power model using TensorFlow Lite settings. Model performance showed a considerable precision of 97,2%, recall of 78,9% and a good F1-score of 0,869. In real-time testing with the wearable device, DL-model showed to be temporally efficient (~2.87 fps) to detect with accuracy doors over real-life scenarios. Future work will include the integration of sensory cues with the developed model in the wearable biofeedback device aiming to validate the final solution with end-users.This work was supported by FCT National Funds, under the National support to R & D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and under the Reference Scholarship under grant SFRH/BD/136569/2018.ElsevierUniversidade do MinhoGonçalves, Helena Raquel Gouveia SilvaSantos, Cristina20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/83191engHelena R. Gonçalves, Cristina P. Santos, Deep learning model for doors detection: A contribution for context-awareness recognition of patients with Parkinson’s disease, Expert Systems with Applications, Volume 212, 2023, 118712, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118712.0957-417410.1016/j.eswa.2022.118712https://doi.org/10.1016/j.eswa.2022.118712info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-16T01:19:16Zoai:repositorium.sdum.uminho.pt:1822/83191Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:37:11.375173Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
title |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
spellingShingle |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease Gonçalves, Helena Raquel Gouveia Silva Object detection Deep-learning RPi Parkinson’s disease Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
title_short |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
title_full |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
title_fullStr |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
title_full_unstemmed |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
title_sort |
Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease |
author |
Gonçalves, Helena Raquel Gouveia Silva |
author_facet |
Gonçalves, Helena Raquel Gouveia Silva Santos, Cristina |
author_role |
author |
author2 |
Santos, Cristina |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gonçalves, Helena Raquel Gouveia Silva Santos, Cristina |
dc.subject.por.fl_str_mv |
Object detection Deep-learning RPi Parkinson’s disease Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
topic |
Object detection Deep-learning RPi Parkinson’s disease Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
description |
Freezing of gait (FoG) is one of the most disabling motor symptoms in Parkinson’s disease, which is described as a symptom where walking is interrupted by a brief, episodic absence, or marked reduction, of forward progression despite the intention to continue walking. Although FoG causes are multifaceted, they often occur in response of environment triggers, as turnings and passing through narrow spaces such as a doorway. This symptom appears to be overcome using external sensory cues. The recognition of such environments has consequently become a pertinent issue for PD-affected community. This study aimed to implement a real-time DL-based door detection model to be integrated into a wearable biofeedback device for delivering on-demand proprioceptive cues. It was used transfer-learning concepts to train a MobileNet-SSD in TF environment. The model was then integrated in a RPi being converted to a faster and lighter computing power model using TensorFlow Lite settings. Model performance showed a considerable precision of 97,2%, recall of 78,9% and a good F1-score of 0,869. In real-time testing with the wearable device, DL-model showed to be temporally efficient (~2.87 fps) to detect with accuracy doors over real-life scenarios. Future work will include the integration of sensory cues with the developed model in the wearable biofeedback device aiming to validate the final solution with end-users. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/83191 |
url |
https://hdl.handle.net/1822/83191 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Helena R. Gonçalves, Cristina P. Santos, Deep learning model for doors detection: A contribution for context-awareness recognition of patients with Parkinson’s disease, Expert Systems with Applications, Volume 212, 2023, 118712, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118712. 0957-4174 10.1016/j.eswa.2022.118712 https://doi.org/10.1016/j.eswa.2022.118712 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799132904767356928 |