Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes how the wearer is currently walking (i.e. gait phase, speed), and then dynamically update the assistance based on the gait state. However, these methods require paired datasets of input kinematics to output gait states, which usually involves manual, time-consuming labeling of data from participants wearing specific exoskeletons and thus limits the scalability of these ML methods. A prior solution to this challenge—leveraging large pre-labeled datasets of normative human walking—introduces another problem, in that networks trained on these datasets learn only normative locomotion patterns, and thus may deteriorate when the data are changed by wearing the exoskeleton itself. In this context, we present an unsupervised-learning-based approach to both bypass the requirement of labeled data for gait state prediction and address the difficulty of domain adaptation from normative to exoskeleton-assisted walking. We validate our method in a set of walking simulations that featured exoskeleton data from 14 participants. This model showed significant improvements in state estimation relative to a model trained solely on pre-labeled normative walking, while also not requiring ground truth labels. This work presents a foundation that demonstrates labeled, device-specific data may not be required for predicting walking behavior in real time.

Exoskeletons have shown great potential to enhance locomotion by augmenting the lower limb. While most research has focused on steady-state ambulatory activities, the ability to assist transient, ballistic tasks is also important for understanding the potential of exoskeletons in mobility enhancement. In this preliminary study (N = 5), we developed an individually-customized control strategy to assist vertical jumping. The control strategy was deployed on bilateral ankle exoskeletons (ExoBoot, Dephy Inc.). We structured the control strategy as a work loop that parameterized the assistance provided during the jump. We show that configuring the controller based on individual biomechanics and user preferences facilitates increased vertical jump height when using exoskeleton assistance. In addition, we demonstrate that a user's squat depth can have a significant (p < 0.05) impact on height achieved, but that this depth does not need to be optimized; rather, the exoskeleton provides the maximum performance assistance from both preferred- and deep-squat conditions. Jump height increased by 7.2% with the exoskeleton at its maximum assistance setting, which is comparable to or greater than previous systems.

Modern exoskeletons are typically developed to optimize for a single, physiological objective, the “gold standard” of which is a reduction of the wearer's metabolic rate. However, recent research suggests that these changes in metabolic rate are not yet perceivable on average. To address this gap, this study explores a novel economic value metric to quantify the value of exoskeleton assistance. The overarching goal of this work is the development of a perceptible metric that leverages the user experience to quantify exoskeleton success. We use the Vickrey second-price auction to obtain the monetary compensation needed for participants to continue walking for consecutive two-minute bouts. Comparing the participant's bidding trends when wearing and not wearing an exoskeleton captures the economic value of the experience, termed Marginal Value (MV). To reduce the logistical burden of the auction, we simulated human participants (robo-bidders) to compete alongside real participants. This work presents a sensitivity analysis to understand how the number and bidding behavior of the robo-bidders affects our economic value metric, MV. We found that MV was not significantly affected by the number of robo-bidders or their bidding behavior (i.e. effort rate). The bidding behavior of the human participants was affected by the robo-bidder effort rate, indicating that there is interplay in the bidding dynamics among the auction participants, but these changes do not significantly affect the marginal value. This study tentatively validates the current approach in generating our proposed metric for exoskeleton success, paving the way for economic value to be further explored as a holistic, personalized metric for the development of lower-limb exoskeletons.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).