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Delayed Output Feedback Control for Gait Assistance and Resistance Using a Robotic Exoskeleton
Bokman Lim, Junwon Jang, Jusuk Lee, Ryungjune Choi, Younbaek Lee, and Youngbo Shim
Abstract—In this study, we propose an interaction control framework for gait assistance and resistance using a robotic exoskeleton. We define ... |
izing oscillatory systems under certain conditions [18]–[20]. By adding a timedelay buffer to the self-feedback control loop, we can generate assistive or resistive torque stably in the interaction between the user and exoskeleton. The proposed interaction controller can operate at various gait speeds and under environ... |
the current sample time and i-1 is the previous sample time. The current smoothed state $y^i$ is expressed as a weighted sum of the previous sample time state $y^{i-1}$ and the original state value of the current sample time $y^i_{raw}$ , and the smoothing rate can be adjusted by changing the smoothing factor $\... |
) are difficult to find. Rather, it is known that metabolic energy greatly increases due to increases in weight and motion constraints due to a rigid frame and actuator structure covering the knee [22]–[24]. The increase in metabolic energy during the device's operation implies that it is a burden to the user even to w... |
2.94 | 0.3 |
| 5 | 3.87 | 3.63 | 6.3 |
| Mean (±SD) | 3.45±0.56 | $3.55 \pm 0.52$ | -3.4±6.5 † |
NMR: net metabolic rate; rNMR: reduced net metabolic rate from free walking condition (No exo). $^\dagger p$ - value = 0.4 > 0.05 for paired t-t... |
The treadmill speed increased from 1 km/h to 5 km/h in 1 km/h increments. The torque and power generated in the hip joint were calculated with a sensor attached to the exoskeleton. As shown in Fig. 5(b), the time-delay $\Delta t$ affects the generated torque magnitude, even though it is a control variable related to... |
$\kappa_2 = 9.5$ | 4.30 | 8.29 | 3.24 | -10.1 |
| $\kappa_3 = 11.0$ | 4.78 | 9.30 | 3.07 | -14.8 |
| $\kappa_4 = 12.5$ | 5.22 | 10.36 | 2.98 | -17.1 |
| $\kappa_5 = 14.0$ | 5.70 | 11.34 | 2.85 | -20.9 |
τRMS ... |
motion (or range of motion) determines the output torque profile. As shown in Fig. 10(c), the generated torque trajectory changed with changes to the gait environment. Without changing the control parameters ( $\Delta t = 0.25~\rm s$ , $\kappa = -4$ ), the generated torque and power values varied with notable differe... |
MEDICAL ROBOTS
Human-in-the-loop optimization of hip assistance with a soft exosuit during walking
Ye Ding, 1,2* Myunghee Kim, 1,2* Scott Kuindersma, 1† Conor J. Walsh 1,2†
Wearable robotic devices have been shown to substantially reduce the energy expenditure of human walking. However, response variance between partic... |
). Although these achievements are impressive, there remain opportunities to explore different wearable assistive hardware, control parameterizations, applications to other joints, and alternative optimization methods that could improve sample efficiency.
We developed an experimental method to rapidly identify optimal ... |
.2% of the conventional gait cycle, defined with heel strike as 0% (table S2). The offset timing was constrained to occur at least 15% later than the peak timing. The range and constraint of peak and offset timing (Fig. 2C) were chosen by slightly extending the average range of the biological hip extension moment (32) ... |
To quantitatively summarize the differences between the participants' metabolic landscapes, we computed the probability that each participant's optimal parameters would reduce the metabolic cost of other participants according to each participant's posterior landscape (Fig. 4, D to F). This analysis suggested that, in... |
a noisy respiratory signal as the objective function of the optimization indicates that this method can be applied to other alternate physiological or biological signals, such as using kinematic symmetry to optimize wearable devices for poststroke patients or using balancerelated measurements to optimize prostheses. T... |
the previous two steps, the reference force profile was scaled for each stride. The actual force signal was measured by two load cells (LSB200, FUTEK Advanced Sensor Technology) placed in series with the Bowden cables on each leg. Combined with the actuator position signals measured by the encoders (AS5134, Ams) mount... |
}) + \varepsilon, \varepsilon \sim N(0, \sigma_{\text{noise}}^2)$$
(4)
where $\sigma_{\text{noise}}^2$ is the noise variance. Given the Gaussian process prior and data set D, the posterior metabolic cost distribution $f_^{\text{inst}}$ was calculated for a parameter $\mathbf{x}_$ as $f_^{\text{inst}}(\mathbf{x}... |
protocol, where the net metabolic rate of the second no-suit condition increased by 32.4% compared with the first no-suit condition.
Convergence time analysis
The convergence time for each participant was calculated in a post hoc analysis (fig. S3 and table S4). We defined the convergence of the optimization with the ... |
The Effect of Hip Assistance Levels on Human Energetic Cost Using Robotic Hip Exoskeletons
Inseung Kang ®, Hsiang Hsu ®, and Aaron Young, Member, IEEE
Abstract—In order for the lower limb exoskeletons to realize their considerable potential, a greater understanding of optimal assistive performance is required. While ot... |
storing the mechanical energy to perform positive joint power. However, due to different muscle characteristics and the lack of efficient elastic storage elements, the hip joint requires higher energetic cost for similar mechanical joint power [20]. Therefore, the hip joint represents an important area of exploration ... |
n}}$$
(2)
Using the both equations, our device can support approximately 90% of peak and maximum continuous torque for an average body mass of 70 kg subject walking at 1.2 m/s. The device allows 100° and 30° range of motion in the sagittal plane for maximum hip flexion and extension respectively. Additionally, a passi... |
3). Over a gait cycle, the controller can generate a torque assistance for both hip flexion and extension with predefined control parameters. The three key parameters that dictate the assistance profile are onset timing, assistance duration, and assistance magnitude. The onset timing parameter was used with values fou... |
keleton without the SEA), exoskeleton in zero impedance mode, and no exoskeleton condition. We have added the actu-
Fig. 4. Initial human characterization results of the exoskeleton device. (A) Actuator interaction torque compensation. When the actuator operates with zero impedance mode, the interaction torque (shown... |
assistance duration window length was swept from 20% to 35% of the gait cycle with a 5% increment. The metabolic cost reduction for each condition was 6.2% for 20%, 14.6% for 25%, 11.2% for 30%, and 11.0% for 35% all relative to metabolic cost of walking in zero impedance mode. As the 25% window length achieved the hi... |
measured using the device's SEA and encoder during walking in each condition.
All of the joint kinematic and kinetic results are represented with an averaged value across 10 subjects. Flexion and extension joint torques were decoupled by computing the torque during flexion ( $45\% \sim 70\%$ ) and extension ( $90\% \s... |
relationship (R2 = 0.999). The stride frequency in zero impedance mode correlated well with human biomechanics data with the same walking speed [28]. A video of a subject walking in a marching gait pattern with hip assistance at maximal levels is included in the supplemental material.
VI. DISCUSSION
Overall, our exosk... |
. Future exoskeleton designers can utilize our findings to further investigate to optimize the mechatronic design for a more robust and versatile exoskeleton. Lastly, implementation of an integrated controller capable of scaling parameters dynamically may aid the exoskeleton technology to be translated to more realisti... |
RESEARCH ARTICLE
Comparing optimized exoskeleton assistance of the hip, knee, and ankle in single and multi-joint configurations
Patrick W. Franks\* , Gwendolyn M. Bryan, Russell M. Martin , Ricardo Reyes, Ava C. Lakmazaheri and Steven H. Collins
Department of Mechanical Engineering, Stanford University, Stanford, Cal... |
et al., 2014; Collins et al., 2015; Seo et al., 2016; Quinlivan et al., 2017; Zhang et al., 2017; Ding et al., 2018; Lee et al., 2018; Malcolm et al., 2018; Lim et al., 2019; MacLean and Ferris, 2019; Cao et al., 2020; Sawicki et al., 2020). The largest metabolic cost reductions have been around 18% relative to walkin... |
this study was to find the single-joint, two-joint, and whole-leg exoskeleton torques that minimized the metabolic cost of walking and to understand how effective each device architecture is at assisting walking. We used a tethered hip–knee–ankle exoskeleton emulator that can assist hip flexion and extension, knee fle... |
., 2020). This device can apply large torques using offboard motors and Bowden cable transmissions to actuate an end effector worn by the user, enabling laboratory tests of different assistance strategies without actuation limits (Caputo and Collins, 2014). The device has a worn mass of 13.5 kg. It has carbon fiber str... |
impedance controller being turned on and off as a function of percent stride defined by the nodes while the knee angle was nonzero, resulting in discrete jumps in desired torque.
testing. Before this, one participant completed a 9-parameter whole-leg optimization pilot study (Supplementary Material, Section 14), which... |
parameter pilot study (Supplementary Material, Section 14). For whole-leg optimization for P1, initial values were based on the optimized values for single-joint assistance. For P2, P3, and P4, initial values for optimization were based on the optimized values for P1. Finally, for the two-joint assistance optimizations... |
was calculated using indirect calorimetry. We measured volumetric carbon dioxide expulsion, oxygen consumption, and breath duration on a breath-by-breath basis (Quark CPET, COSMED). For each condition, we calculated metabolic rate using a modified Brockway equation (Brockway, 1987) similar to previous studies (Zhang e... |
assistance reduced metabolic cost for each participant, with an average reduction of 13% relative to walking with no torque, although this was not statistically significant (N = 3, range of reductions: 5–18%, p = .07). Ankle-only assistance performed best of the single-joint strategies, reducing metabolic cost by 30% ... |
knees, whole-leg assistance optimized to smaller magnitudes than single-joint assistance. For the ankles, maximum torque had to be constrained to find comfortable profiles for walking. Ankle torques were limited to 1 Nm/kg for single-joint assistance, and 0.8 Nm/kg for two-joint and whole-leg assistance.
in multi-join... |
7; Ding et al., 2018; Lim et al., 2019). This corresponded to a 33% reduction relative to walking with no exoskeleton, much greater than the just-noticeable difference in metabolic cost (20%) (Medrano et al., 2020), indicating that participants could feel the reduction in effort compared to walking normally. Because wh... |
user bent more into flexion, allowing for a "stabilizing" effect that prevented buckling of the knee. In this study, we used a tethered exoskeleton emulator to compare assistance, but future work should attempt to recreate this assistance on mobile exoskeletons. We reported our improvements primarily relative to walki... |
studying two-joint exoskeleton assistance could confirm the results found here.
These results suggest that new cost functions, gait environments, and user populations could be exciting topics for future studies. Future work could optimize metabolic cost alongside other costs that are important for gait, such as walkin... |
Task-agnostic exoskeleton control via biological joint moment estimation
https://doi.org/10.1038/s41586-024-08157-7
Received: 17 October 2023
Accepted: 4 October 2024
Published online: 13 November 2024
Dean D. Molinaro1,2,5,8 ⋈, Keaton L. Scherpereel1,2,6,8, Ethan B. Schonhaut1, Georgios Evangelopoulos3,7, Max K. Shep... |
13,37-39}$ . Additionally,
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA. 3X, The Moonshot Factory, Mountain View, CA, USA. 4College of Engineering, Bouvé College ... |
, Nanchang) mounted coaxially with the hip and knee provided up to 15 N m of assistance at each joint. The semirigid structure consisted of carbon fibre and 3D printed nylon orthotics on which the actuators and sensors were mounted. Six inertial measurement units (IMUs), joint encoders on the hips and knees, and a pair... |
knee moments significantly better than the baseline method for both cyclic (hip R2 0.79, knee R2 0.86) and impedance-like activities (hip R2 0.81, knee R2 0.87) without any participant-specific calibration (Fig. 3b,c). Representative time series are shown in Fig. 3d–f. Comparing within each activity, our estimator sig... |
described in Extended Data Table 2, that had not been previously tested or analysed (Supplementary Fig. 1 and Supplementary Video 5). These tasks were intentionally designed to be highly unique from the original dataset to push the limits of our approach, including burpees, mimicking a basketball layup and walking on ... |
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