Predicting walking response to ankle exoskeletons using data-driven models
TL;DR: The ability of subject-specific phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics, is evaluated.
Abstract: II Abstract Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging We evaluated the ability of three classes of subject-specific phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics Each model – phase-varying (PV), linear phase-varying (LPV), and nonlinear phase-varying (NPV) – leveraged Floquet Theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy For twelve unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions The LPV model’s predictions were more accurate than the PV model when predicting less than 125% of a stride in the future and explained 49–70% of the variance in hip, knee, and ankle kinematic responses to torque The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses This work highlights the potential of data-driven phase-varying models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control
Summary (1 min read)
- The authors expected the NPV model’s prediction accuracy to meet or exceed that of the 192 LPV model.
- The amount of data required to accurately predict response to exoskeletons will restrict the settings in which 252 phase-varying models are practical, such as in clinical gait analysis where datasets typically contain only a 253 few gait cycles [2, 8].
- To test each model’s generalizability across a range of exoskeleton torque conditions, the authors separately predicted 261 responses to torque in the K1, K2, and K3 datasets, termed held-out conditions, at a 12.5% stride prediction 262 horizon (1/8th of a stride).
VI. DISCUSSION 336
- The authors evaluated the ability of subject-specific phase-varying models to predict kinematic and myoelectric 337 responses to ankle exoskeleton torques during treadmill walking.
- 351 the LPV model’s predictions explained more of the variance in kinematic responses to exoskeletons than the 352 PV model, regardless of whether predictions interpolated (K1 and K2) or extrapolated (K3) relative to the 353 training set.
VII. CONCLUSION 459
- To their knowledge, this is the first study to predict subject-specific responses to ankle exoskeletons using 460 phase-varying models.
- Without making assumptions about individual physiology or motor control, an LPV 461 model predicted short-time kinematic responses to bilateral passive ankle exoskeletons, though predicting 462 myoelectric responses remains challenging.
- Results support the utility of LPV models for studying and 463 predicting response to exoskeleton torque.
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Frequently Asked Questions (2)
Q1. What are the contributions in "Predicting walking response to ankle exoskeletons using data-driven models" ?
In this paper, the authors predict subject-specific responses to ankle exoskeletons using 460 phase-varying models.
Q2. What are the future works in "Predicting walking response to ankle exoskeletons using data-driven models" ?
Improving data-driven models and experimental protocols to study 464 and predict myoelectric responses to exoskeletons represents an important direction for future research. 465 Modeling responses to exoskeletons or other assistive devices using a phase-varying perspective has the 466 potential to inform exoskeleton design for a range of user groups.