Predicting walking response to ankle exoskeletons using data-driven models
Summary (1 min read)
Introduction
- 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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References
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"Predicting walking response to ankl..." refers background in this paper
...59 However, musculoskeletal properties and motor control are highly uncertain for individuals with motor 60 impairments, today’s most ubiquitous ankle exoskeleton users [19, 20, 22, 23]....
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...Additionally, when specific measurement sets are unavailable for model parameter tuning, population58 average based assumptions about musculoskeletal properties and motor control are required [17, 20-22]....
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178 citations
"Predicting walking response to ankl..." refers background or methods in this paper
...remains challenging for unimpaired individuals and those with motor impairments [2, 12, 13]....
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...kinematic or myoelectric) 36 control laws, predicting responses over even 10–20% of a stride may improve tracking performance or 37 transitions between control modes [4, 10-12]....
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...distributed between the initial and final phases – to the inputs, resulting in N = 80 inputs [6, 12]....
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...activity, we found that the average unperturbed gait cycle accounted for only 30-60% of the variance in the 418 K2 data, compared to 60-95% in kinematic signals [1, 9, 12]....
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166 citations
"Predicting walking response to ankl..." refers background or methods in this paper
...Ankle exoskeletons are used to improve kinematics and reduce the energetic demands of locomotion in 27 unimpaired adults and individuals with neurologic injuries [1-5]....
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...Guided adaptation and extended practice sessions [1, 17] or powered ankle exoskeletons [5, 6] may 410...
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...147 The marker trajectories were low-pass filtered at 6 Hz using a zero-lag fourth-order Butterworth filter [5]....
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...These small changes may correspond to larger changes in joint powers or metabolic 350 demands and indicate that the present study is a rigorous test case [1, 2, 5, 24]....
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136 citations
"Predicting walking response to ankl..." refers background in this paper
...kinematic or myoelectric) 36 control laws, predicting responses over even 10–20% of a stride may improve tracking performance or 37 transitions between control modes [4, 10-12]....
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...The LPV and NPV models’ accurate predictions over short prediction horizons make 407 them primarily useful for exoskeleton control [10, 11]....
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Frequently Asked Questions (2)
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.