Abstract--For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.

Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.

This letter presents a model-agnostic meta-learning (MAML) based framework for simultaneous and accurate estimation of human gait phase and terrain geometry using a small set of fabric-based wearable soft sensors, with efficient adaptation to unseen subjects and strong generalization across different subjects and terrains. Compared to rigid alternatives such as inertial measurement units, fabric-based soft sensors improve comfort but introduce nonlinearities due to hysteresis, placement error, and fabric deformation. Moreover, inter-subject and inter-terrain variability, coupled with limited calibration data in real-world deployments, further complicate accurate estimation. To address these challenges, the proposed framework integrates MAML into a deep learning architecture to learn a generalizable model initialization that captures subject- and terrain-invariant structure. This initialization enables efficient adaptation (i.e., adaptation with only a small amount of calibration data and a few fine-tuning steps) to new users, while maintaining strong generalization (i.e., high estimation accuracy across subjects and terrains). Experiments on nine participants walking at various speeds over five terrain conditions demonstrate that the proposed framework outperforms baseline approaches in estimating gait phase, locomotion mode, and incline angle, with superior accuracy, adaptation efficiency, and generalization.

Gait phase estimation based on inertial measurement unit (IMU) signals facilitates precise adaptation of exoskeletons to individual gait variations. However, challenges remain in achieving high accuracy and robustness, particularly during periods of terrain changes. To address this, we develop a gait phase estimation neural network based on implicit modeling of human locomotion, which combines temporal convolution for feature extraction with transformer layers for multi-channel information fusion. A channel-wise masked reconstruction pre-training strategy is proposed, which first treats gait phase state vectors and IMU signals as joint observations of human locomotion, thus enhancing model generalization. Experimental results demonstrate that the proposed method outperforms existing baseline approaches, achieving a gait phase RMSE of $2.729 \pm 1.071%$ and phase rate MAE of $0.037 \pm 0.016%$ under stable terrain conditions with a look-back window of 2 seconds, and a phase RMSE of $3.215 \pm 1.303%$ and rate MAE of $0.050 \pm 0.023%$ under terrain transitions. Hardware validation on a hip exoskeleton further confirms that the algorithm can reliably identify gait cycles and key events, adapting to various continuous motion scenarios. This research paves the way for more intelligent and adaptive exoskeleton systems, enabling safer and more efficient human-robot interaction across diverse real-world environments.

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants engaging in treadmill walking and stair ascent and descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power suggesting exoskeleton assistance across the different conditions.

Recognizing and identifying human locomotion is a critical step to ensuring fluent control of wearable robots, such as transtibial prostheses. In particular, classifying the intended locomotion mode and estimating the gait phase are key. In this work, a novel, interpretable, and computationally efficient algorithm is presented for simultaneously predicting locomotion mode and gait phase. Using able-bodied (AB) and transtibial prosthesis (PR) data, seven locomotion modes are tested including slow, medium, and fast level walking (0.6, 0.8, and 1.0 m/s), ramp ascent/descent (5 degrees), and stair ascent/descent (20 cm height). Overall classification accuracy was 99.1$\%$ and 99.3$\%$ for the AB and PR conditions, respectively. The average gait phase error across all data was less than 4$\%$. Exploiting the structure of the data, computational efficiency reached 2.91 $\mu$s per time step. The time complexity of this algorithm scales as $O(N\cdot M)$ with the number of locomotion modes $M$ and samples per gait cycle $N$. This efficiency and high accuracy could accommodate a much larger set of locomotion modes ($\sim$ 700 on Open-Source Leg Prosthesis) to handle the wide range of activities pursued by individuals during daily living.

With rapid advancements in exoskeleton hardware technologies, successful assessment and accurate control remain challenging. This study introduces a modular sensor-based system to enhance biomechanical evaluation and control in lower-limb exoskeletons, utilizing advanced sensor technologies and fuzzy logic. We aim to surpass the limitations of current biomechanical evaluation methods confined to laboratories and to address the high costs and complexity of exoskeleton control systems. The system integrates inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components function both independently and collectively to capture comprehensive biomechanical data, including the anteroposterior center of pressure and crutch ground reaction forces. This data is processed through a central unit using fuzzy logic algorithms for real-time gait phase estimation and exoskeleton control. Validation experiments with three participants, benchmarked against gold-standard motion capture and force plate technologies, demonstrate our system's capability for reliable gait phase detection and precise biomechanical measurements. By offering our designs open-source and integrating cost-effective technologies, this study advances wearable robotics and promotes broader innovation and adoption in exoskeleton research.

The current state-of-the-art in quadruped locomotion is able to produce robust motion for terrain traversal but requires the segmentation of a desired robot trajectory into a discrete set of locomotion skills such as trot and crawl. In contrast, in this work we demonstrate the feasibility of learning a single, unified representation for quadruped locomotion enabling continuous blending between gait types and characteristics. We present Gaitor, which learns a disentangled representation of locomotion skills, thereby sharing information common to all gait types seen during training. The structure emerging in the learnt representation is interpretable in that it is found to encode phase correlations between the different gait types. These can be leveraged to produce continuous gait transitions. In addition, foot swing characteristics are disentangled and directly addressable. Together with a rudimentary terrain encoding and a learned planner operating in this structured latent representation, Gaitor is able to take motion commands including desired gait type and characteristics from a user while reacting to uneven terrain. We evaluate Gaitor in both simulated and real-world settings on the ANYmal C platform. To the best of our knowledge, this is the first work learning such a unified and interpretable latent representation for multiple gaits, resulting in on-demand continuous blending between different locomotion modes on a real quadruped robot.

Powered hip exoskeletons have shown the ability for locomotion assistance during treadmill walking. However, providing suitable assistance in real-world walking scenarios which involve changing terrain remains challenging. Recent research suggests that forecasting the lower limb joint's angles could provide target trajectories for exoskeletons and prostheses, and the performance could be improved with visual information. In this letter, We share a real-world dataset of 10 healthy subjects walking through five common types of terrain with stride-level label. We design a network called Sandwich Fusion Transformer for Image and Kinematics (SFTIK), which predicts the thigh angle of the ensuing stride given the terrain images at the beginning of the preceding and the ensuing stride and the IMU time series during the preceding stride. We introduce width-level patchify, tailored for egocentric terrain images, to reduce the computational demands. We demonstrate the proposed sandwich input and fusion mechanism could significantly improve the forecasting performance. Overall, the SFTIK outperforms baseline methods, achieving a computational efficiency of 3.31 G Flops, and root mean square error (RMSE) of 3.445 \textpm \ 0.804\textdegree \ and Pearson's correlation coefficient (PCC) of 0.971 \textpm\ 0.025. The results demonstrate that SFTIK could forecast the thigh's angle accurately with low computational cost, which could serve as a terrain adaptive trajectory planning method for hip exoskeletons. Codes and data are available at https://github.com/RuoqiZhao116/SFTIK.

Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We propose a transformer-based probabilistic framework, termed the Angle Estimation Probabilistic Model (AEPM), that offers precise angle estimations across extensive scenarios beyond walking. AEPM achieves an overall RMSE of 6.70 degrees, with an RMSE of 3.45 degrees in walking scenarios. Compared to the state of the art, AEPM has improved the prediction accuracy for walking by 11.31%. Our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses. The code is available at https://github.com/penway/Beyond-Gait-AEPM.