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Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and
Benjamin R Hindle1, Justin W L Keogh1,2,3,4, Anna V Lorimer1,2
1Faculty of Health Sciences and Medicine, Bond University, Gold Coast 4226, Australia.
Applied Bionics and Biomechanics
|April 16, 2021
Summary
Inertial-based motion capture (IMC) offers advantages over traditional systems, but its accuracy depends on data processing. This summary aids researchers in developing better IMC methods for human movement analysis.
Area of Science:
- Biomechanics
- Human Movement Analysis
- Wearable Technology
Background:
- Inertial-based motion capture (IMC) presents a viable alternative to traditional systems, addressing limitations in accessibility and cost.
- The accuracy and validity of IMC are critically dependent on the specific data processing methodologies employed.
- Standardized and advanced data processing techniques are crucial for reliable human spatiotemporal and kinematic measurements.
Purpose of the Study:
- To provide a foundational technical summary for researchers and developers in Inertial-based motion capture (IMC).
- To outline current data processing methodologies for estimating human spatiotemporal and kinematic measures using IMC.
- To guide further development and standardization of IMC data processing practices.
Main Methods:
- Review of common methodologies for processing raw inertial sensor data.
- Discussion of techniques for estimating spatiotemporal measures (e.g., stride length, stride rate) including thresholding and zero-velocity updates.
- Overview of methods for joint kinematics estimation, focusing on sensor-to-segment alignment and filtering techniques (e.g., Kalman, complementary filters).
Main Results:
- Identified measurement thresholding and zero-velocity updates as primary methods for spatiotemporal measure estimation.
- Highlighted Kalman filtering or complementary filtering combined with various sensor-to-segment alignment techniques as prevalent for joint kinematics.
- Discussed factors affecting IMC accuracy, including soft tissue artifacts, device placement, biomechanical modeling, and environmental interference.
Conclusions:
- Further research and development are essential to minimize estimation errors and enhance the validity of IMC.
- Standardization of data processing practices is needed to improve the reliability and comparability of IMC results.
- This technical summary aims to accelerate the development and adoption of advanced IMC methodologies.
