Learning Task-adaptive Quasi-stiffness Control for A Powered Transfemoral Prosthesis

While significant advancements have been made in the mechanical and task-specific controller designs of powered transfemoral prostheses, developing a task-adaptive control framework that generalizes across various locomotion modes and terrain conditions remains an open problem. This study proposes a task-adaptive learning quasi-stiffness control framework for powered prostheses that generalizes across tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. Quasi-stiffness is defined as the slope of the human joint's torque-angle relationship. To accurately obtain the torque-angle relationship in a new task, a Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint's angle and torque in the task. Then a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of a new task from multiple human demonstrations and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speed and incline walking tasks. The proposed framework has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

SLLEN: Semantic-aware Low-light Image Enhancement Network

How to effectively explore semantic feature is vital for low-light image enhancement (LLE). Existing methods usually utilize the semantic feature that is only drawn from the semantic map produced by high-level semantic segmentation network (SSN). However, if the semantic map is not accurately estimated, it would affect the high-level semantic feature (HSF) extraction, which accordingly interferes with LLE. In this paper, we develop a simple yet effective two-branch semantic-aware LLE network (SLLEN) that neatly integrates the random intermediate embedding feature (IEF) (i.e., the information extracted from the intermediate layer of semantic segmentation network) together with the HSF into a unified framework for better LLE. Specifically, for one branch, we utilize an attention mechanism to integrate HSF into low-level feature. For the other branch, we extract IEF to guide the adjustment of low-level feature using nonlinear transformation manner. Finally, semantic-aware features obtained from two branches are fused and decoded for image enhancement. It is worth mentioning that IEF has some randomness compared to HSF despite their similarity on semantic characteristics, thus its introduction can allow network to learn more possibilities by leveraging the latent relationships between the low-level feature and semantic feature, just like the famous saying "God rolls the dice" in Physics Nobel Prize 2022. Comparisons between the proposed SLLEN and other state-of-the-art techniques demonstrate the superiority of SLLEN with respect to LLE quality over all the comparable alternatives.

A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.