Session: 06-05-01: Biomedical Devices, Sensors, and Actuators

Paper Number: 145524

145524 - Markerless Pose Estimation and Wearable Sensor Technology for Enhanced Diagnosis and Monitoring of Idiopathic Toe Walking 

Idiopathic Toe Walking (ITW) presents a unique challenge in clinical diagnosis and management. This condition, characterized by a persistent toe-to-toe gait without any identifiable cause, is notably prevalent among individuals with intellectual and developmental disabilities, such as autism spectrum disorder (ASD). The consequences of untreated ITW can be far-reaching, leading to physiological complications such as skeletal and muscular deformations, which can profoundly impact physical function and social well-being. Despite its significance, current diagnostic methods for ITW are limited, relying primarily on manual step counting from video recordings. Moreover, these assessments are typically conducted in specialized behavioral analysis facilities and lack the capability for continuous monitoring, leaving clinicians with incomplete information about the patient's gait patterns outside of controlled settings. Recognizing the need for improved diagnostic and monitoring tools, this study introduces a novel approach that integrates markerless pose estimation technology and wearable sensor technology to address the challenges associated with diagnosing and monitoring ITW. The markerless pose estimation method represents a significant advancement in the field, enabling clinicians to accurately assess toe and heel pose coordinates and derive the contact angle normal to the floor. By analyzing these parameters, clinicians can identify distinctive gait patterns associated with idiopathic toe walking, facilitating more objective and reliable diagnoses. In addition to the markerless pose estimation technique, this study introduces wearable sensor technology for continuous motion monitoring. A 6-axis inertial measurement unit (IMU) attached to the shoe provides clinicians with real-time data on motion values, allowing for the continuous monitoring of gait dynamics. This continuous monitoring capability is particularly valuable as it enables clinicians to track changes in gait patterns over time, providing insights into the progression of ITW and the effectiveness of intervention strategies. The preliminary results demonstrate the feasibility of the proposed method in accurately detecting ITW steps from normal steps and the ability to record the toe angle in real-time. This validation underscores the potential of the integrated approach in enhancing the diagnosis and management of ITW. By combining these technologies, clinicians can access quantitative methods for diagnosing ITW and monitoring patient rehabilitation in real-time. This not only enhances the accuracy and efficiency of ITW diagnosis but also holds the potential to improve patient outcomes and quality of life by enabling timely intervention and personalized treatment approaches. In summary, this study presents a comprehensive approach to addressing the diagnostic and monitoring challenges associated with ITW. By leveraging state-of-the-art technologies, clinicians can gain deeper insights into this complex condition, ultimately leading to improved patient care and outcomes.

Presenting Author: Mohammad Ahmed Florida Institute of technology

Presenting Author Biography: Mr. Ahmed is a PhD candidate in Florida Institute of Technology. His research focus is in the areas of physiological measurements, biomedical signal processing and application of machine learning to develop novel diagnostic and monitoring solutions.

Authors:

Yasith Weerasinghe Florida Institute of Technology
Michael Grillo Florida Institute of Technology
Mohammad Ahmed Florida Institute of technology
Christina Sheppard Florida Institute of Technology
Amirtaha Taebi Mississippi State University
David Wilder Florida Institute of Technology
Mehmet Kaya Florida Institute of Technology
Peshala Thibbotuwawa Gamage Florida Institute of Technology