Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation
Abstract This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from th...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50727-8 |
| _version_ | 1797355799309189120 |
|---|---|
| author | Mohammad Ali Takallou Farahnaz Fallahtafti Mahdi Hassan Ali Al-Ramini Basheer Qolomany Iraklis Pipinos Sara Myers Fadi Alsaleem |
| author_facet | Mohammad Ali Takallou Farahnaz Fallahtafti Mahdi Hassan Ali Al-Ramini Basheer Qolomany Iraklis Pipinos Sara Myers Fadi Alsaleem |
| author_sort | Mohammad Ali Takallou |
| collection | DOAJ |
| description | Abstract This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data. |
| first_indexed | 2024-03-08T14:17:19Z |
| format | Article |
| id | doaj.art-90c136590e884a98b97b75b287f4ec8e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:17:19Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-90c136590e884a98b97b75b287f4ec8e2024-01-14T12:20:24ZengNature PortfolioScientific Reports2045-23222024-01-0114111410.1038/s41598-023-50727-8Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementationMohammad Ali Takallou0Farahnaz Fallahtafti1Mahdi Hassan2Ali Al-Ramini3Basheer Qolomany4Iraklis Pipinos5Sara Myers6Fadi Alsaleem7Architectural Engineering Department, University of Nebraska–LincolnDepartment of Biomechanics, University of Nebraska at OmahaDepartment of Biomechanics, University of Nebraska at OmahaMechanical Engineering Department, University of Nebraska-LincolnCyber Systems Department, University of Nebraska at KearneyDepartment of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care SystemDepartment of Biomechanics, University of Nebraska at OmahaArchitectural Engineering Department, University of Nebraska–LincolnAbstract This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data.https://doi.org/10.1038/s41598-023-50727-8 |
| spellingShingle | Mohammad Ali Takallou Farahnaz Fallahtafti Mahdi Hassan Ali Al-Ramini Basheer Qolomany Iraklis Pipinos Sara Myers Fadi Alsaleem Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation Scientific Reports |
| title | Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation |
| title_full | Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation |
| title_fullStr | Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation |
| title_full_unstemmed | Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation |
| title_short | Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation |
| title_sort | diagnosis of disease affecting gait with a body acceleration based model using reflected marker data for training and a wearable accelerometer for implementation |
| url | https://doi.org/10.1038/s41598-023-50727-8 |
| work_keys_str_mv | AT mohammadalitakallou diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT farahnazfallahtafti diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT mahdihassan diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT alialramini diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT basheerqolomany diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT iraklispipinos diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT saramyers diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation AT fadialsaleem diagnosisofdiseaseaffectinggaitwithabodyaccelerationbasedmodelusingreflectedmarkerdatafortrainingandawearableaccelerometerforimplementation |