Inertial sensor-based knee flexion/extension angle estimation |
| |
| Authors: | Glen Cooper Ian Sheret Louise McMillian Konstantinos Siliverdis Ning Sha Diana Hodgins Laurence Kenney David Howard |
| |
| Institution: | 1. Centre for Rehabilitation and Human Performance Research, University of Salford, UK;2. Analyticon, Tessella Support Services Plc, Stevenage, UK;3. European Technology for Business Ltd, Codicote, UK;1. Institute for Systems Engineering and Computers – Technology and Science (INESC TEC), and Faculty of Engineering (FEUP), University of Porto, 4200-391 Porto, Portugal;2. Institute of Electronics and Informatics Engineering of Aveiro (IEETA), and Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;1. Department of Mechanical and Aerospace Engineering, “Sapienza” University of Rome, Via Eudossiana, 18, 00184 Rome, Italy;2. Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Division, IRCCS Children’s Hospital “Bambino Gesù”, Via Torre di Palidoro, 00050 Passoscuro, Fiumicino, Rome, Italy;3. Department of Economics and Management – Industrial Engineering, University of Tuscia, Via del Paradiso, 47, 01100 Viterbo, Italy;4. Robotics Brain and Cognitive Science Department, Italian Institute of Technology (IIT), Genoa, Italy |
| |
| Abstract: | A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 mile/h. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5 mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
