The need for the temporal alignment of gait cycle data is well known; however, there is little consensus concerning which alignment method to use. In this paper, we discuss the pros and cons of some methods commonly applied to temporally align gait cycle data (normalization to percent gait cycle, dynamic time warping, derivative dynamic time warping, and piecewise alignment methods). In addition, we empirically evaluate these different methods' abilities to produce successful temporal alignment when mapping a test gait cycle trajectory to a target trajectory. We demonstrate that piecewise temporal alignment techniques outperform other commonly used alignment methods (normalization to percent gait cycle, dynamic time warping, and derivative dynamic time warping) in typical biomechanical and clinical alignment tasks. Lastly, we present an example of how these piecewise alignment techniques make it possible to separately examine intensity and temporal differences between gait cycle data throughout the entire gait cycle, which can provide greater insight into the complexities of movement patterns.
Bibliographical noteFunding Information:
This work was supported by the NSF ( #0727083 ) and Mary Jane Neer Disability Research Fund at the University of Illinois. Thanks to Louis DiBerardino, K. Alex Shorter, Prof. Karl Rosengren, and anonymous reviewers for their assistance and comments.
- Curve registration
- Gait analysis
- Temporal alignment
- Time normalization
- Time warping