Lower Limb Kinetic Prediction While WalkingBased on Machine Learning AlgorithmsUsing IMU Sensor
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CARSE07_032
تاریخ نمایه سازی: 5 تیر 1402
Abstract:
The use of artificial neural network (ANN) approaches on data from inertial measurement units (IMUs) for prediction has been reported in recent publications. These techniques could be used as quantitative markers of athletic performance or rehabilitation. The quantity and composition of IMUs. The selection of these parameter values is often made heuristically, and the justification for this is not discussed. We suggest employing an ANN to forecast the dynamic data of the lower limbs using a single measurement point based on the dynamic link between the center of mass and joint kinetics. From a single IMU worn close to the sacrum, data from seven subjects walking on a treadmill at various speeds were gathered. The data was divided into steps and given a numerical treatment for integration. From the kinematics of the measurement from a single IMU sensor, segment angles of the stance and swing leg and joint torques were estimated with fair accuracy. These findings highlight the significance of dynamic multi-segment kinetics during walking. A machine-learning approach based on the dynamic features of human walking can be used to resolve the tradeoff between data volume and wearable convenience.
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Authors
Iman Bagheri
Biomedical Engineering, Imam Reza International University
Mahdi Bagheri
Computer Engineering, Khavaran Institute of Higher Education
Ali Ahmadi
Civil Engineering, Iran University of Science and Tech,
Amirreza Rouhbakhsh
Electronic Engineering, Northwestern Polytechnic University
Amirhossein Amadeh
Independent Researcher
Somia Molaei
Software Engineering, Iran University of Industries & Mines