Lower limb dysfunction hinders the locomotive activity in individuals and compels them to use assistive devices such as prostheses or orthoses to restore the missing locomotive functions. Passive assistive devices have limitations in replacing the lost functionality. On the other hand, active devices could restore more natural locomotion using motorized joints. To make proper use of these embedded motors, a control architecture is required to translate the motion intent of the user into the intended prosthesis joint trajectories. In this article, we propose a temporal convolution-based online foot angle trajectory prediction network (FATP-N) that predicts the foot angular positions during unconstrained walking from the shank angular position measured using a single wearable motion tracker sensor. The data acquisition experiments were designed to reflect natural gait with turns, varying inclines, speeds, and mixed cadences. The trained models were prepared for real-time prediction by compressing them to reduce the storage and time complexity. The validations were performed on different motion conditions, including those that have not been used for model training, to verify the robustness of the model. The proposed FATP-N achieved high accuracy and could generate predictions in real time. The results indicate that the proposed approach offers a possible architecture for real-time control of powered intelligent prostheses.

Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for predictive detection of potential failures. Accordingly, we trigger gait switching merely based on proprioceptive sensory information. To learn this predictive model, we propose a semi-supervised process for detecting and annotating ground truth slip events in two stages: We first detect abnormal occurrences in the time series sequences of the gait data using an unsupervised anomaly detector, and then, the anomalies are verified with expert human knowledge in a replay simulation to assert the event of a slip. These annotated slip events are then used as ground truth examples to train an ensemble decision learner for predicting slip probabilities across terrains for traversability. We analyze our model on data recorded by a legged robot on multiple sites with slippery terrain. We demonstrate that a potential slip event can be predicted up to 720 ms ahead of a potential fall with an average precision greater than 0.95 and an average F-score of 0.82. Finally, we validate our approach in real-time by deploying it on a legged robot and switching its gait mode based on slip event detection.

Dexterous control of upper limb prostheses with multiarticulated wrists/hands is still a challenge due to the limitations of myoelectric man-machine interfaces. Multiple factors limit the overall performance and usability of these interfaces, such as the need to control degrees of freedom sequentially and not concurrently, and the inaccuracies in decoding the user intent from weak or fatigued muscles. In this article, we developed a novel man-machine interface that endows a myoelectric prosthesis (MYO) with artificial perception, estimation of user intention, and intelligent control (MYO-PACE) to continuously support the user with automation while preparing the prosthesis for grasping. We compared the MYO-PACE against state-of-the-art myoelectric control (pattern recognition) in laboratory and clinical tests. For this purpose, eight able-bodied and two amputee individuals performed a standard clinical test consisting of a series of manipulation tasks (portion of the SHAP test), as well as a more complex sequence of transfer tasks in a cluttered scene. In all tests, the subjects not only completed the trials faster using the MYO-PACE but also achieved more efficient myoelectric control. These results demonstrate that the implementation of advanced perception, context interpretation, and autonomous decision-making into active prostheses improves control dexterity. Moreover, it also effectively supports the user by speeding up the preshaping phase of the movement and decreasing muscle use.

One of the main challenges of the active assistive devices is how to estimate the motion of the missing/impaired limbs and joints in line with the remaining limbs. To do so, a motion planner is required. This study proposes a motion planner that can be used for active prosthetic/orthotic knees. The aim is to continuously estimate the knee joint positions based on the thigh motion, using as few inputs as possible. Data from thigh-mounted IMU (thigh acceleration and angle) are used as inputs to estimate knee joint positions as outputs. It is aimed to continuously estimate the outputs as opposed to the state-machine approaches which divide the gait cycles into different sections and require switching rules. The performance of the motion planner is investigated for five walking speeds (0.6, 0.9, 1.2, 1.4 and 1.6 m/s). The strengths and limitations of the motion planer are investigated at different scenarios.