Gait analysis using gravitational acceleration measured by wearable sensors |
| |
| Authors: | Ryo Takeda Shigeru Tadano Masahiro Todoh Manabu Morikawa Minoru Nakayasu Satoshi Yoshinari |
| |
| Institution: | 1. Division of Human Mechanical Systems and Design, Graduate School of Engineering, Hokkaido University, N13 W8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan;2. Human Engineering Section, Product Technology Department, Hokkaido Industrial Research Institute, Sapporo, Japan;1. Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, USA;2. Department of Human Physiology, University of Oregon, Eugene, OR 97403, USA;3. Safety and Health Assessment and Research for Prevention (SHARP) Program, Washington State Department of Labor and Industries, Olympia, WA 98504, USA;4. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, ROC;1. Faculty of Kinesiology, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada;2. Faculty of Nursing, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada;3. Running Injury Clinic, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada;1. Institute of General Mechanics, RWTH Aachen University, Germany;2. Polytechnique School of Engineering, University of Pernambuco, Recife, Brazil;3. Institute of Biomechanics and Orthopedics, German Sport University Cologne, Germany;1. Department of Physical Medicine and Rehabilitation, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA;2. Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA;3. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA;1. Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK;2. INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK;3. MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA), The University of Sheffield, Sheffield, UK;1. Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom;2. Computer Laboratory, University of Cambridge, Cambridge, United Kingdom;3. Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom;4. Cardiff University, Wales, United Kingdom |
| |
| Abstract: | A novel method for measuring human gait posture using wearable sensor units is proposed. The sensor units consist of a tri-axial acceleration sensor and three gyro sensors aligned on three axes. The acceleration and angular velocity during walking were measured with seven sensor units worn on the abdomen and the lower limb segments (both thighs, shanks and feet). The three-dimensional positions of each joint are calculated from each segment length and joint angle. Joint angle can be estimated mechanically from the gravitational acceleration along the anterior axis of the segment. However, the acceleration data during walking includes three major components; translational acceleration, gravitational acceleration and external noise. Therefore, an optimization analysis was represented to separate only the gravitational acceleration from the acceleration data. Because the cyclic patterns of acceleration data can be found during constant walking, a FFT analysis was applied to obtain some characteristic frequencies in it. A pattern of gravitational acceleration was assumed using some parts of these characteristic frequencies. Every joint position was calculated from the pattern under the condition of physiological motion range of each joint. An optimized pattern of the gravitational acceleration was selected as a solution of an inverse problem. Gaits of three healthy volunteers were measured by walking for 20 s on a flat floor. As a result, the acceleration data of every segment was measured simultaneously. The characteristic three-dimensional walking could be shown by the expression using a stick figure model. In addition, the trajectories of the knee joint in the horizontal plane could be checked by visual imaging on a PC. Therefore, this method provides important quantitive information for gait diagnosis. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
