Recent work has shown that exoskeletons con-trolled through data-driven methods can dynamically adapt assistance to various tasks for healthy young adults. However, applying these methods to populations with neuromotor gait deficits, such as post-stroke hemiparesis, is challenging. This is due not only to high population heterogeneity and gait variability but also to a lack of post-stroke gait datasets to train accurate models. Despite these challenges, data-driven methods offer a promising avenue for control, potentially allowing exoskeletons to function safely and effectively in unstructured community settings. This work presents a first step towards enabling adaptive plantarflexion and dorsiflexion assistance from data-driven torque estimation during post-stroke walking. We trained a multi-task Temporal Convolutional Network (TCN) using collected data from four post-stroke participants walking on a treadmill (R2of 0.74 ± 0.13). The model uses data from three inertial measurement units (IMU) and was pretrained on healthy walking data from 6 participants. We implemented a wearable prototype for our ankle torque estimation approach for exoskeleton control and demonstrated the viability of real-time sensing, estimation, and actuation with one post-stroke participant.

Community-based locomotor training post-stroke has shown improvements in independent ambulation by increasing dose, intensity, and specificity of walking practice. Robotic ankle exoskeletons hold the potential to facilitate continued rehabilitation at home, but understanding what aspects of the design are most relevant for successful translation to the community presents a challenge. Here, we design a portable rigid ankle exoskeleton to use as a research platform for investigating the effect of assistance on post-stroke gait during overground, community-based walking. We first test our device with stroke survivors and validate its potential for future community use. We then present a systematic method for quantifying power transmission losses at each transmission stage from the battery to the wearer, using data gathered from walking trials with healthy participants. Our evaluation method revealed inefficiencies in power transfer at the interface level, likely resulting from the compliance in the structural components of the system, which motivates future redesign considerations. Overall, our method provides a framework to identify and characterize the components that must be redesigned to lower exoskeleton weight and maximize performance.