13 accepted papers
Ankle exoskeletons have the potential to augment mobility, but control strategies have largely failed to seamlessly adapt to changes in the locomotion task. Here, we introduce a multi-headed network that predicts gait speed, ground incline, stance/swing transitions, and percent stance. These predict
Traditional robotic knee-ankle prostheses categorize ambulation modes such as level walking, ramps, and stairs. However, human movement scales continuously across various states rather than discretely, making traditional mode classifiers inadequate for accurate intent recognition. This paper propose
Post-stroke gait is slow, energetically costly, and unstable. Rehabilitation is necessary to encourage, retrain, and assist proper gait mechanics in stroke survivors. Evidence indicates robotic ankle exoskeletons can improve gait outcomes in stroke survivors, however challenges remain with proper lo
Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This s
Deep learning offers a potentially powerful alternative to hand-tuned control of active lower-limb prostheses, being capable of generating continuous joint-level assistance end-to-end. This eliminates the need for conventional task classification, state machines and mid-level control equations by co
Humans are required to maintain balance during locomotion in challenging environments, which present an even bigger challenge for individuals with balance impairments. Exoskeleton-driven balance augmentation is a promising avenue to assist users in these environments, but there has been little work
Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. I…
Ankle exoskeletons have the potential to improve mobility, but common controllers are often inflexible to variations in tasks, such as changes in walking speed. To enable effective variable-speed exoboot control, we developed and validated a two-headed convolutional neural network trained to (1) cla
Dynamically altering the parameters for assistance in a lower limb prosthesis is a challenge that depends directly on the ability to estimate gait parameters. Machine learning algorithms present an opportunity to develop methods for continuously determining walking speed in different conditions. Cur
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
Ground slope incline is a critical environmental variable that influences exoskeleton control parameters since human biological joint demand is correlated to changes in slope incline. Current literature methods take a heuristic approach by numerically calculating the slope incline from on-board mech
Developing intelligent prosthetic controllers to recognize user intent across users is a challenge. Machine learning algorithms present an opportunity to develop methods for predicting user's locomotion mode. Currently, linear discriminant analysis (LDA) offers the standard solution in the state-of-
In order for the lower limb exoskeletons to realize their considerable potential, a greater understanding of optimal assistive performance is required. While others have shown positive results, the fundamental question of how the exoskeleton interacts with the human remains unknown. Understanding th