text stringlengths 710 6.67k |
|---|
exes", that receive joint position information, ground contact and virtual muscle lengths as inputs, and generate activation signals for the virtual muscles. Tis concept was initially proposed as a model that can reproduce gait patterns similar to the natural human gait.
Te refex model has often been used to control pr... |
to compensate the gravitational forces such that the leg is approximately in static equilibrium in all confgurations.
Tis method can also make the paretic leg follow the motion of the healthy limb in people with asymmetric pathologies [272], but this method is usable only if the movements of both legs should be symmet... |
of the more advanced controllers have focused on adding more compliant behavior to the position controller, such as the socalled "proxy-based sliding mode controller" [189, 293, 294], which ofers smooth and gradual recovery in case of large errors. An iterative (over several gait cycles) online optimization of the tor... |
performance "transparency" (zero torque) without the need for a torque sensor [114]. In [26], the knee is position-controlled, but also features passive variable stifness thanks to an additional actuator that controls the pre-tension of a spring. Achieving such compliance with a single actuator would not be possible, b... |
as well.
Metabolic cost
Te metabolic cost is the amount of energy consumed by a subject to complete a task. Tese methods are useful because they capture the power exerted by the user, which relates closely to the required "efort". However, the human body adapts slowly (with response times on the order of 1min [317]) a... |
or in general, if there is only one mode of operation). Tis layer is central to the performance of assistive devices and consequently, the existing studies in exoskeleton literature are predominantly focused on this part. Tis layer is also usually more heavily afected by the fact that the controller is intended for an ... |
| PN | State machine. Transitions using threshold on the feet pressure sensors (two per foot, one in front and one in back). Dorsiflexion torque applied at heel strike and toe-off, no assistance during foot flat, plantarflexion torque applied at heel off ... |
of the 285 reviewed control strategies included low-level controllers, although in some cases the type was not clearly mentioned. 60 control strategies were
Fig. 7 Percentage of the considered publications that addressed high/mid/low level, per year of publication
based on position control and 186 based on torque con... |
is rarely used because the user has not enough voluntary control of the legs. For the lower layers, pre-defned gait trajectories with position-control (LNP+PPR+POS) are successful in practise, because they are simple to implement and reliable, and it is possible to tune them for diferent types of gait and obstacles. T... |
controllers with machine-learning-based environment/activity recognition and synchronization can become more common. Robust and reliable detection of the terrain or the activity mode using machine learning can make exoskeletons more autonomous and much easier to use in everyday situations. Accurate detection of the ga... |
unable to command the exoskeleton accurately and quickly enough. Consequently, more research work on efcient user interfaces could improve the current generation of exoskeletons and make them quicker in less structured environments (single step stair, short sideways slope, speed bump, etc.).
For partial assistance, th... |
Preference-Based Learning for Exoskeleton Gait Optimization
Maegan Tucker∗1 , Ellen Novoseller∗2 , Claudia Kann1 , Yanan Sui3 , Yisong Yue2 , Joel W. Burdick1,2 , and Aaron D. Ames1,2
Abstract—This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical ob... |
briefly explain this method to illustrate how it can be adapted based on user preferences; for more details, refer to [3]–[5].
Partial Hybrid Zero Dynamics Method. Systems with impulse effects, such as ground impacts, can be represented as hybrid control systems [21]–[23]. Summarizing from [4], the natural system dyna... |
over gait B?) to determine the gait parameters most preferred by the user [13], [14], [16], [27]–[29], since preference feedback has been shown to be much more reliable than absolute feedback when learning from subjective human responses [13], [30]. Thus, our goal to personalize the exoskeleton's gait can be framed as... |
(7)
The posterior P(f|D) can be estimated via the Laplace approximation as a multivariate Gaussian distribution; see [35] for details. Finally, in formulating the posterior, preferences can be weighted relatively to one another if some are thought to be noisier than others. This is accomplished by changing σ to σk in ... |
= 0.025, signal variance = 0.0001, noise variance = 1e-8; preference noise (σ) = 0.01
Fig. 2. Leftmost: COT for the CG biped at different step lengths and a fixed 0.2 m/s velocity. Remaining plots: posterior utility estimates of COSPAR (n = 2, b = 0; without coactive feedback) after varying iterations of learning (po... |
preference mean at each step length. COSPAR draws more samples in the region of higher posterior preference.
Fig. 4 shows the simulation results. In each case, the mixed-initiative simulations involving coactive feedback improve upon those with only preferences. Learning is slowest for n = 2, b = 0 (Fig. 4), since tha... |
85 and 1.15 seconds (with 10% and 20% modifications under coactive feedback), and step width into 6 values between 0.25 and 0.30 meters (20% and 40%). After each trial, the user was queried for both a pairwise preference and coactive feedback. Fig. 6 shows the results for both feature spaces. The estimated preference v... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.