کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Optimizing Dual IMU Sensor Placement for Gait Phase Detection with LSTM Models

نویسندگان :

Mahya Abedi¹ Zolfa Anvari² Hamed Ghafarirad³ Mohammad Zareinejad⁴

1- دانشگاه صنعتی امیرکبیر 2- دانشگاه صنعتی امیرکبیر 3- دانشگاه صنعتی امی 4- دانشگاه صنعتی امیر

کلمات کلیدی :

Gait phase detection،IMU sensors،sensor placement evaluation،LSTM network،real-time optimization،quantization techniques

چکیده :

Precise analysis of human gait phases is essential for diagnosing motor disorders, optimizing athletic performance, and designing assistive devices. However, achieving high accuracy in gait phase detection while maintaining real-time applicability poses significant challenges, primarily due to suboptimal sensor placement and latency constraints. This study addresses these challenges by optimizing the placement of Inertial Measurement Unit (IMU) sensors and applying quantization techniques to enhance real-time performance. Using a Long Short-Term Memory (LSTM) algorithm, four key gait phases—heel strike, toe strike, heel off, and toe-off—were analyzed across five distinct sensor configurations. Training features were uniquely selected for each configuration based on a systematic feature selection process to ensure optimal model performance. Experiments under controlled walking conditions demonstrated that the placement of sensors on the shin and bridge of the foot achieved the highest accuracy (97.4%), followed by the thigh and bridge of the foot (96.34%). Additionally, quantization techniques reduced latency to 3–5 milliseconds without compromising accuracy, highlighting the feasibility of real-time applications. The findings of this study establish a foundation for selecting optimal sensor placements tailored to specific accuracy and performance requirements. Researchers and practitioners can leverage these configurations to compare detection accuracy against their unique application needs, facilitating the design of efficient and practical gait analysis systems for medical, rehabilitation, and sports contexts.