Gait speed is a key metric of gait function and recovery in stroke survivors. This study presents a novel approach for estimating gait speed using a single inertial measurement unit (IMU) placed on the pelvis and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX). To validate the network, datasets from two distinct cohorts of stroke survivors were collected: a chronic group of 40 community-dwelling individuals, and an inpatient group of 42 participants recruited from an inpatient rehabilitation facility (IRF). The NARX model was initially developed using data from the chronic group and subsequently refined using data from the inpatient group. The model's performance was evaluated using data from the 10-Meter Walk Test (10MWT) and the 6-Minute Walk Test (6MWT). Results demonstrated high temporal alignment between the NARX-estimated gait speeds and those derived from a reference system (i.e., Xsens MVN) during the 10MWT, with a median cross-correlation coefficient of 0.98 and a median mean squared error of 0.05 (m/S)2. When applied to the 6MWT, the NARX model accurately estimated the total distance walked with a median absolute error of 41.66 m. The proposed method enables continuous gait speed and distance monitoring in stroke survivors with minimal setup using a single IMU. This approach would facilitate in-person and remote patient monitoring, offering valuable insights for personalized rehabilitation strategies for stroke survivors in IRFs.

Robotic lower limb prostheses have the power to revolutionize mobility by enhancing gait efficiency and facilitating movement. While several design approaches have been explored to create lightweight and energy-efficient devices, the potential of underactuation remains largely untapped in lower limb prosthetics. Taking inspiration from the natural harmony of walking, in this article, we have developed an innovative active transfemoral prosthesis. By incorporating underactuation, our design uses a single power actuator placed near the knee joint and connected to a differential mechanism to drive both the knee and ankle joints. We conduct comprehensive benchtop tests and evaluate the prosthesis with three individuals who have above-knee amputations, assessing its performance in walking, stair climbing, and transitions between sitting and standing. Our evaluation focuses on gathering position and torque data recorded from sensors integrated into the prosthesis and comparing these measurements to biomechanical data of able-bodied locomotion. Our findings highlight the promise of underactuation in advancing lower limb prosthetics and demonstrate the feasibility of our knee–ankle underactuated design in various tasks, showcasing its ability to replicate natural movement.

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a singleadaptiveDMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor.