Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat
Deep learning, a subfield of artificial intelligence that uses neural networks with multiple layers, is rapidly changing healthcare. Its ability to analyze large datasets and extract relevant information makes it a powerful tool for improving diagnosis, treatment, and disease management. The integra...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/12050 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846105909541994496 |
|---|---|
| author | Iker Azurmendi Manuel Gonzalez Gustavo García Ekaitz Zulueta Elena Martín |
| author_facet | Iker Azurmendi Manuel Gonzalez Gustavo García Ekaitz Zulueta Elena Martín |
| author_sort | Iker Azurmendi |
| collection | DOAJ |
| description | Deep learning, a subfield of artificial intelligence that uses neural networks with multiple layers, is rapidly changing healthcare. Its ability to analyze large datasets and extract relevant information makes it a powerful tool for improving diagnosis, treatment, and disease management. The integration of DL with pressure mats—which are devices that use pressure sensors to continuously and non-invasively monitor the interaction between patients and the contact surface—is a promising application. These pressure platforms generate data that can be very useful for detecting postural anomalies. In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. We investigated several main tasks: patient classification, hemibody segmentation, recognition of specific body parts, and generation of automated clinical reports. For this purpose, convolutional neural networks in their classification and regression modalities, the object detection algorithm YOLOv8, and the open language model LLaMa3 were used. Our results demonstrated high accuracy in all tasks: classification achieved 100% accuracy; hemibody division obtained an MAE of approximately 7; and object detection had an average accuracy of 70%. These results demonstrate the potential of this approach for monitoring postural and motor disabilities. By enabling personalized patient care, our methodology contributes to improved clinical outcomes and healthcare delivery. To our best knowledge, this is the first study that combines pressure images with multiple deep learning algorithms for the detection and assessment of postural disorders and motor disabilities in this group of patients. |
| format | Article |
| id | doaj-art-37a8b5d9c9e24df6a0f2716a2f70b25f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-37a8b5d9c9e24df6a0f2716a2f70b25f2024-12-27T14:09:13ZengMDPI AGApplied Sciences2076-34172024-12-0114241205010.3390/app142412050Deep Learning-Based Postural Asymmetry Detection Through Pressure MatIker Azurmendi0Manuel Gonzalez1Gustavo García2Ekaitz Zulueta3Elena Martín4Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, SpainDepartment of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, SpainCS Centro Stirling S. Coop., Avda. Álava 3, 20550 Aretxabaleta, SpainDepartment of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 01006 Vitoria-Gasteiz, SpainPhysiotherapy Area, Special Education, Care and Rehabilitation School “El Camino”, Association of Parents of People with Cerebral Palsy and Related Encephalopathies (ASPACE) of Salamanca, Camino Alto a los Villares, 12, 37185 Salamanca, SpainDeep learning, a subfield of artificial intelligence that uses neural networks with multiple layers, is rapidly changing healthcare. Its ability to analyze large datasets and extract relevant information makes it a powerful tool for improving diagnosis, treatment, and disease management. The integration of DL with pressure mats—which are devices that use pressure sensors to continuously and non-invasively monitor the interaction between patients and the contact surface—is a promising application. These pressure platforms generate data that can be very useful for detecting postural anomalies. In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. We investigated several main tasks: patient classification, hemibody segmentation, recognition of specific body parts, and generation of automated clinical reports. For this purpose, convolutional neural networks in their classification and regression modalities, the object detection algorithm YOLOv8, and the open language model LLaMa3 were used. Our results demonstrated high accuracy in all tasks: classification achieved 100% accuracy; hemibody division obtained an MAE of approximately 7; and object detection had an average accuracy of 70%. These results demonstrate the potential of this approach for monitoring postural and motor disabilities. By enabling personalized patient care, our methodology contributes to improved clinical outcomes and healthcare delivery. To our best knowledge, this is the first study that combines pressure images with multiple deep learning algorithms for the detection and assessment of postural disorders and motor disabilities in this group of patients.https://www.mdpi.com/2076-3417/14/24/12050healthcarepostural asymmetrypressure matartificial intelligencedeep learninggenerative AI |
| spellingShingle | Iker Azurmendi Manuel Gonzalez Gustavo García Ekaitz Zulueta Elena Martín Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat Applied Sciences healthcare postural asymmetry pressure mat artificial intelligence deep learning generative AI |
| title | Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat |
| title_full | Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat |
| title_fullStr | Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat |
| title_full_unstemmed | Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat |
| title_short | Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat |
| title_sort | deep learning based postural asymmetry detection through pressure mat |
| topic | healthcare postural asymmetry pressure mat artificial intelligence deep learning generative AI |
| url | https://www.mdpi.com/2076-3417/14/24/12050 |
| work_keys_str_mv | AT ikerazurmendi deeplearningbasedposturalasymmetrydetectionthroughpressuremat AT manuelgonzalez deeplearningbasedposturalasymmetrydetectionthroughpressuremat AT gustavogarcia deeplearningbasedposturalasymmetrydetectionthroughpressuremat AT ekaitzzulueta deeplearningbasedposturalasymmetrydetectionthroughpressuremat AT elenamartin deeplearningbasedposturalasymmetrydetectionthroughpressuremat |