Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

Mohammad Ali Takallou; Farahnaz Fallahtafti; Mahdi Hassan; Ali Al-Ramini; Basheer Qolomany; Iraklis Pipinos; Sara Myers; Fadi Alsaleem

doi:10.1038/s41598-023-50727-8

Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

Sci Rep. 2024 Jan 11;14(1):1075. doi: 10.1038/s41598-023-50727-8.

Authors

Mohammad Ali Takallou¹, Farahnaz Fallahtafti^{2

3}, Mahdi Hassan^{2

3}, Ali Al-Ramini⁴, Basheer Qolomany⁵, Iraklis Pipinos^{3

6}, Sara Myers^{2

3}, Fadi Alsaleem⁷

Affiliations

¹ Architectural Engineering Department, University of Nebraska-Lincoln, Omaha, NE, 68182, USA.
² Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, 6160, USA.
³ Department of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA.
⁴ Mechanical Engineering Department, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
⁵ Cyber Systems Department, University of Nebraska at Kearney, Kearney, NE, 68849, USA.
⁶ Department of Surgery, University of Nebraska Medical Center, Omaha, NE, 68105, USA.
⁷ Architectural Engineering Department, University of Nebraska-Lincoln, Omaha, NE, 68182, USA. falsaleem2@unl.edu.

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.

MeSH terms

Acceleration
Accelerometry
Gait
Humans
Peripheral Arterial Disease*
Wearable Electronic Devices*