Estimating human joint moments unifies exoskeleton control, reducing user effort.

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

Effects of Bilateral Assistance for Hemiparetic Gait Post-Stroke Using a Powered Hip Exoskeleton.

Hemiparetic gait due to stroke is characterized by an asymmetric gait due to weakness in the paretic lower limb. These inter-limb asymmetries increase the biomechanical demand and reduce walking speed, leading to reduced community mobility and quality of life. With recent progress in the field of wearable technologies, powered exoskeletons have shown great promise as a potential solution for improving gait post-stroke. While previous studies have adopted different exoskeleton control methodologies for restoring gait post-stroke, the results are highly variable due to limited understanding of the biomechanical effect of exoskeletons on hemiparetic gait. In this study, we investigated the effect of different hip exoskeleton assistance strategies on gait function and gait biomechanics of individuals post-stroke. We found that, compared to walking without a device, powered assistance from hip exoskeletons improved stroke participants' self-selected overground walking speed by 17.6 ± 2.5% and 11.1 ± 2.7% with a bilateral and unilateral assistance strategy, respectively (p < 0.05). Furthermore, both bilateral and unilateral assistance strategies significantly increased the paretic and non-paretic step length (p < 0.05). Our findings suggest that powered assistance from hip exoskeletons is an effective means to increase walking speed post-stroke and tuning the balance of assistance between non-paretic and paretic limbs (i.e., a bilateral strategy) may be most effective to maximize performance gains.

Subject-Independent Continuous Locomotion Mode Classification for Robotic Hip Exoskeleton Applications.

Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 ± 0.38% and transitional: 6.49 ± 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation.

Effect of Assistance Timing in Knee Extensor Muscle Activation During Sit-to-Stand Using a Bilateral Robotic Knee Exoskeleton.

The population of older adults experiences a significant degradation in musculoskeletal structure, which hinders daily physical activities. Standing up from a seated position is difficult for mobility-challenged individuals since a significant amount of knee extensor moment is required to lift the body's center of mass. One solution to reduce the required muscle work during sit-to-stand is to utilize a powered exoskeleton system that can provide relevant knee extension assistance. However, the optimal exoskeleton assistance strategy for maximal biomechanical benefit is unknown for sit-to-stand tasks. To answer this, we explored the effect of assistance timing using a bilateral robotic exoskeleton on the user's knee extensor muscle activation. Assistance was provided at both knee joints from 0% to 65% of the sit-to-stand movement, with a maximum torque occurring at four different timings (10%, 25%, 40%, and 55%). Our experiment with five able-bodied subjects showed that the maximal benefit in knee extensor activation, 19.3% reduction, occurred when the assistance timing was delayed relative to the user's biological joint moment. Among four assistance conditions, two conditions with each peak occurring at 25% and 40% significantly reduced the muscle activation relative to the no assistance condition (p < 0.05). Additionally, our study results showed a U-shaped trend (R2= 0.93) in the user's muscle activation where the global optimum occurred between 25% and 40% peak timing conditions, indicating that there is an optimal level of assistance timing in maximizing the exoskeleton benefit.

Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network.

Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs.

Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion.

We developed and validated a gait phase estimator for real-time control of a robotic hip exoskeleton during multimodal locomotion. Gait phase describes the fraction of time passed since the previous gait event, such as heel strike, and is a promising framework for appropriately applying exoskeleton assistance during cyclic tasks. A conventional method utilizes a mechanical sensor to detect a gait event and uses the time since the last gait event to linearly interpolate the current gait phase. While this approach may work well for constant treadmill walking, it shows poor performance when translated to overground situations where the user may change walking speed and locomotion modes dynamically. To tackle these challenges, we utilized a convolutional neural network-based gait phase estimator that can adapt to different locomotion mode settings to modulate the exoskeleton assistance. Our resulting model accurately predicted the gait phase during multimodal locomotion without any additional information about the user's locomotion mode, with a gait phase estimation RMSE of 5.04 ± 0.79%, significantly outperforming the literature standard (p < 0.05). Our study highlights the promise of translating exoskeleton technology to more realistic settings where the user can naturally and seamlessly navigate through different terrain settings.

Biomechanical Comparison of Assistance Strategies Using a Bilateral Robotic Knee Exoskeleton.

Despite there being studies that have investigated the effects of human augmentation using a knee exoskeleton, comparing different assistance schemes on a single knee exoskeleton has not been studied. Using a light-weight, low-profile bilateral knee exoskeleton system, this study examined and compared the biomechanical effects of three common assistance strategies (biological torque, impedance, and proportional myoelectric controllers) exhibiting different levels of flexibility for the user to control the assistance. Nine subjects walked on a 15% gradient incline surface at 1.1 m/s in the three powered conditions and with the exoskeleton unpowered. All the assistance strategies significantly reduced the metabolic cost of the users compared to the unpowered condition by 3.0% on average across strategies (p < 0.05), led by the significant reduction in the biological knee kinetic effort and knee extensor muscle activation (p < 0.05). Between assistance strategies, the metabolic cost and biomechanics displayed no statistically significant differences. The metabolic and biomechanical results indicate that powered extension assistance during early stance can improve performance compared to the unpowered condition. However, the user's ability to control the assistance may not be significant for human augmentation when walking on an inclined surface with a knee exoskeleton.

The exoskeleton expansion: improving walking and running economy.

Since the early 2000s, researchers have been trying to develop lower-limb exoskeletons that augment human mobility by reducing the metabolic cost of walking and running versus without a device. In 2013, researchers finally broke this 'metabolic cost barrier'. We analyzed the literature through December 2019, and identified 23 studies that demonstrate exoskeleton designs that improved human walking and running economy beyond capable without a device. Here, we reviewed these studies and highlighted key innovations and techniques that enabled these devices to surpass the metabolic cost barrier and steadily improve user walking and running economy from 2013 to nearly 2020. These studies include, physiologically-informed targeting of lower-limb joints; use of off-board actuators to rapidly prototype exoskeleton controllers; mechatronic designs of both active and passive systems; and a renewed focus on human-exoskeleton interface design. Lastly, we highlight emerging trends that we anticipate will further augment wearable-device performance and pose the next grand challenges facing exoskeleton technology for augmenting human mobility.

Effects of Assistance During Early Stance Phase Using a Robotic Knee Orthosis on Energetics, Muscle Activity, and Joint Mechanics During Incline and Decline Walking.

The knee joint performs a significant amount of positive or negative mechanical work during gradient walking, and targeted assistance during periods of high mechanical work could yield strong human augmentation benefits. This paper explores the biomechanical effects of providing knee extension assistance during the early stance phase of the gait cycle using a powered unilateral knee exoskeleton during gradient walking on able-bodied subjects. Twelve subjects walked on 15% gradient incline and decline surfaces with the exoskeleton providing knee extension assistance during the early stance phase of the gait cycle. For both incline and decline walking, the exoskeleton assistance reduced the muscle activation of the knee extensors on the assisted leg ( ). However, only approximately half the individuals responded to exoskeleton assistance positively by reducing their metabolic cost of walking for both incline and decline tasks. The results indicate that, unlike the individuals who did respond, the individuals who did not respond to the assistance may have penalized their metabolic cost by their biomechanical compensatory behaviors from the unassisted leg.

Continuous locomotion mode classification using a robotic hip exoskeleton.

Human augmentation through robotic exoskeleton technology can enhance the user's mobility for a wide range of ambulation tasks. This is done by providing assistance that is in line with the user's movement during different locomotion modes (e.g., ramps and stairs). Several machine learning techniques have been applied to classify such tasks on lower limb prostheses, but these strategies have not been applied extensively to exoskeleton systems which often rely on similar control inputs. Additionally, conventional methods often identify modes at a discrete time during the gait cycle which can delay the corresponding assistance to the user and potentially reduce overall exoskeleton benefit. We developed a gait phase-based Bayesian classifier that can classify five ambulation modes continuously throughout the gait cycle using only mechanical sensors on the device. From our five able-bodied subject experiment with a robotic hip exoskeleton, we found that implementing multiple models within the gait cycle can reduce the classification error rate by 35% compared to using a single model (p < 0.05). Furthermore, we found that utilizing bilateral sensor information can reduce the error by 43% compared to using a unilateral information (p < 0.05). Our study findings provide valuable information for future exoskeleton developers to utilize different on-board mechanical sensors to enhance mode classification for a faster update rate in the controller and provide more natural and seamless exoskeleton assistance between locomotion modes.

Biological Hip Torque Estimation using a Robotic Hip Exoskeleton.

Machine learning (ML) algorithms present an opportunity to estimate joint kinetics using a limited set of mechanical sensors. These estimates could be used as a continuous reference signal for exoskeleton control, able to modulate exoskeleton assistance in real-world environments. In this study, sagittal plane biological hip torque during level ground, incline and decline walking was calculated using inverse dynamics of human subject data. Subsequently, this torque was estimated using neural network (NN) and XGBoost ML models. Model inputs consisted solely of mechanical sensor data onboard a robotic hip exoskeleton. These results were compared to a baseline method of estimating hip torque as the mean torque profile during ambulation. On average across conditions, the NN and XGBoost models estimated biological hip torque with an RMSE of 0.116±0.015 and 0.108±0.011 Nm/kg, respectively, which was significantly less than the baseline estimation that had an RMSE of 0.300±0.145 Nm/kg (p<0.05). Fitting the baseline method to ambulation mode specific data significantly reduced overall RMSE by 59.3%; however, the ML models were still significantly better than the baseline method (p<0.05). These results show that machine learning algorithms can estimate biological hip torque using only mechanical sensors onboard a hip exoskeleton better than simply using an average torque profile. This suggests that these estimation models could be suitable for modulating exoskeleton assistance. Additionally, no evidence suggested the need to train separate ML models for each ambulation mode as estimation RMSE was not significantly different across unified and separated ML models.

A Semi-Wearable Robotic Device for Sit-to-Stand Assistance.

With the aging of the population in the United States, an increasing number of individuals suffer from mobility challenges. For such individuals, the difficulty of standing up from a seated position is a major barrier for their daily physical activities. In the paper, a novel assistive device, namely Semi-Wearable Sit-to-Stand Assist (SW-SiStA), is presented, which provides effective lower-limb assistance to overcome such difficulty for the mobility-challenged individuals. Unlike traditional exoskeletons, the SW-SiStA can be easily detached after the completion of the sit-to-stand process, and thus will not cause additional burden to the user during the subsequent ambulation. The SW-SiStA is powered with a pneumatic actuator, leverage its advantages of low cost and high power/force density. The complexity of the device is reduced by the use of a simple solenoid valve in combination with two adjustable needle valves, providing the desired individualized adjustability without the expensive proportional valves. Human testing of the device indicated that the SW-SiStA was able to provide effective sit-to-stand assistance in a natural way, and the users were able to expend significantly less muscle efforts in the process.

Electromyography (EMG) Signal Contributions in Speed and Slope Estimation Using Robotic Exoskeletons.

Robotic exoskeletons have the capability to improve community ambulation in aging individuals. These exoskeleton controllers utilize different environmental information such as walking speeds and slope inclines to provide corresponding assistance. Several numerical approaches for estimating this environmental information have been implemented; however, they tend to be limited during dynamic changes. A possible solution is a machine learning model utilizing the user's electromyography (EMG) signals along with mechanical sensor data. We developed a neural network-based walking speed and slope estimator for a powered hip exoskeleton and explored the EMG signal contributions in both static and dynamic settings while wearing the device. We also analyzed the performance of different EMG electrode placements. The resulting machine learning model achieved error rates below 0.08 m/s RMSE and 1.3 RMSE. Our study findings from four able-bodied and two elderly subjects indicate that EMG can improve the performance by reducing the error rate by 14.8% compared to the model using only mechanical sensors. Additionally, results show that using EMG electrode configuration within the exoskeleton interface region is sufficient for the EMG model performance.