An official website of the United States government
Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.
more »
« less
Trust and Pediatric Exoskeletons: A Comparative Study of Clinician and Parental Perspectives
The purpose of this study was to survey the perspectives of clinicians regarding pediatric robotic exoskeletons and compare their views with the views of parents of children with disabilities. A total of 78 clinicians completed the survey; they were contacted through Children’s Healthcare of Atlanta, the American Academy for Cerebral Palsy and Developmental Medicine, and group pages on Facebook. Most of the clinicians were somewhat concerned to very concerned that a child might not use the device safely outside of the clinical setting. Most clinicians reported that the child would try to walk, run, and climb using the exoskeleton. The parents reported higher trust (i.e., lower concern) in the child using an exoskeleton outside of the clinical setting, compared to the clinician group. Prior experience with robotic exoskeletons can have an important impact on each group’s expectations and self-reported level of trust in the technology.
more »
« less
- Award ID(s):
- 1849101
- PAR ID:
- 10139609
- Date Published:
- Journal Name:
- IEEE Transactions on Technology and Society
- ISSN:
- 2637-6415
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Detection of the user’s walking is a critical part of exoskeleton technology for the full automation of smooth and seamless assistance during movement transitions. Researchers have taken several approaches in developing a walk detection system by using different kinds of sensors; however, only a few solutions currently exist which can detect these transitions using only the sensors embedded on a robotic hip exoskeleton (i.e., hip encoders and a trunk IMU), which is a critical consideration for implementing these systems in-the-loop of a hip exoskeleton controller. As a solution, we explored and developed two walk detection models that implemented a finite state machine as the models switched between walking and standing states using two transition conditions: stand-to-walk and walk-to-stand. One of our models dynamically detected the user’s gait cycle using two hip encoders and an IMU; the other model only used the two hip encoders. Our models were developed using a publicly available dataset and were validated online using a wearable sensor suite that contains sensors commonly embedded on robotic hip exoskeletons. The two models were then compared with a foot contact estimation method, which served as a baseline for evaluating our models. The results of our online experiments validated the performance of our models, resulting in 274 ms and 507 ms delay time when using the HIP+IMU and HIP ONLY model, respectively. Therefore, the walk detection models established in our study achieve reliable performance under multiple locomotive contexts without the need for manual tuning or sensors additional to those commonly implemented on robotic hip exoskeletons.more » « less
-
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.more » « less
-
null (Ed.)Several COVID-19 vaccines have been on the market since early 2021 and may vary in their effectiveness and safety. This study characterizes hesitancy about accepting COVID-19 vaccines among parents in Shanghai, China, and identifies how sensitive they are to changes in vaccine safety and effectiveness profiles. Schools in each township of Minhang District, Shanghai, were sampled, and parents in the WeChat group of each school were asked to participate in this cross-sectional Internet-based survey. Parents responded to questions about hesitancy and were given information about five different COVID-19 vaccine candidates, the effectiveness of which varied between 50 and 95% and which had a risk of fever as a side effect between 5 and 20%. Overall, 3673 parents responded to the survey. Almost 90% would accept a vaccine for themselves (89.7%), for their child (87.5%) or for an elderly parent (88.5%) with the most ideal attributes (95% effectiveness with 5% risk of fever). But with the least ideal attributes (50% effectiveness and a 20% risk of fever) these numbers dropped to 33.5%, 31.3%, and 31.8%, respectively. Vaccine hesitancy, age at first child’s birth, and relative income were all significantly related to sensitivity to vaccine safety and effectiveness. Parents showed a substantial shift in attitudes towards a vaccine based on its safety and effectiveness profile. These findings indicate that COVID-19 vaccine acceptance may be heavily influenced by how effective the vaccine actually is and could be impeded or enhanced based on vaccines already on the market.more » « less
-
Exoskeletons have the potential to support daily activities by assisting the movement performance. Previous studies have shown that powered elbow exoskeletons can reduce muscle effort during continuous cyclic movement. However, natural movements have embedded stationary hold periods as well as transitions between postures. The human performance when using exoskeletons for more natural movements should be evaluated. In this study, we implemented visual and haptic electromyography (EMG) biofeedback to help people use an upper limb exoskeleton to perform a target position matching task. Participants (n=36) did not significantly reduce their muscle effort during hold periods when provided with biofeedback. Participants had difficulty relaxing their muscles at more flexed postures during hold periods, suggesting that they continued to provide effort instead of taking advantage of the device. To fully benefit from robotic exoskeletons, additional training and more advanced controllers might be needed.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/TTS.2020.2974990
