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Inertial measurement units (IMUs) could provide an attractive alternative to video motion capture systems for measuring walking in a non-laboratory setting. This study applied an optimization method to an 18-DOF lower body model to convert synthetic IMU data into corresponding joint angles.
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Evaluation of a Nonlinear Optimization Method for Measuring Human Movement Using Inertial Measurement Units
Inertial measurement units (IMUs) are an alternative to traditional optical motion capture systems that allow for data collection outside the lab and continuous monitoring for daily activities. In this study, a non-linear least squares optimization was used to convert IMU measurements into joint kinematics. The optimization calculates joint angles simultaneously over all time frames by optimizing B-spline nodes, without integrating any IMU measurements. This approach enables an accurate solution that works well with noisy experimental IMU data since integration errors are eliminated.
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- Award ID(s):
- 1805896
- PAR ID:
- 10355173
- Date Published:
- Journal Name:
- Proceedings of the 9th World Congress of Biomechanics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The objective of this study was to test the feasibility of using a pair of wearable inertial measurement unit (IMU) sensors to accurately capture dynamic joint motion data during simulated occupational conditions. Eleven subjects (5 males and 6 females) performed repetitive neck, low-back, and shoulder motions simulating low- and high-difficulty occupational tasks in a laboratory setting. Kinematics for each of the 3 joints were measured via IMU sensors in addition to a “gold standard” passivemarker optical motion capture system. The IMU accuracy was benchmarked relative to the optical motion capture system, and IMU sensitivity to low- and high-difficulty tasks was evaluated. The accuracy of the IMU sensors was found to be very good on average, but significant positional drift was observed in some trials. In addition, IMU measurements were shown to be sensitive to differences in task difficulty in all 3 joints (P < .05). These results demonstrate the feasibility for using wearable IMU sensors to capture kinematic exposures as potential indicators of occupational injury risk. Velocities and accelerations demonstrate the most potential for developing risk metrics since they are sensitive to task difficulty and less sensitive to drift than rotational position measurements.more » « less
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