IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues

2024

Article

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Wearable sensing using inertial measurement units (IMUs) is enabling portable and customized gait retraining for knee osteoarthritis. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. This study investigated how kinematic errors impact an individual's ability to learn a therapeutic gait using vibrotactile cues. Sensor accuracy was computed by comparing the IMU-based foot progression angle to marker-based motion capture, which was used as ground truth. Thirty subjects were randomized into three groups to learn a toe-in gait: one group received vibrotactile feedback during gait retraining in the laboratory, another received feedback outdoors, and the control group received only verbal instruction and proceeded directly to the evaluation condition. All subjects were evaluated on their ability to maintain the learned gait in a new outdoor environment. We found that subjects with high tracking errors exhibited more incorrect responses to vibrotactile cues and slower learning rates than subjects with low tracking errors. Subjects with low tracking errors outperformed the control group in the evaluation condition, whereas those with higher error did not. Errors were correlated with foot size and angle magnitude, which may indicate a non-random algorithmic bias. The accuracy of IMU-based kinematics has a cascading effect on feedback; ignoring this effect could lead researchers or clinicians to erroneously classify a patient as a non-responder if they did not improve after retraining. To use patient and clinician time effectively, future implementation of portable gait retraining will require assessment across a diverse range of patients.

@article{Rokhmanova24-TNSRE-Accuracy,
  title = {{IMU}-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues},
  author = {Rokhmanova, Nataliya and Pearl, Owen and Kuchenbecker, Katherine J. and Halilaj, Eni},
  journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  volume = {32},
  pages = {1005--1012},
  month = feb,
  year = {2024},
  doi = {10.1109/TNSRE.2024.3365204},
  month_numeric = {2}
}