We seek to predict knee and ankle motion using wearable sensors. These predictions could serve as target trajectories for a lower limb prosthesis. In this manuscript, we investigate the use of egocentric vision for improving performance over kinematic wearable motion capture. We present an out-of-the-lab dataset of 23 healthy subjects navigating public classrooms, a large atrium, and stairs for a total of almost 12 hours of recording. The prediction task is difficult because the movements include avoiding obstacles, other people, idiosyncratic movements such as traversing doors, and individual choices in selecting the future path. We demonstrate that using vision improves the quality of the predicted knee and ankle trajectories, especially in congested spaces and when the visual environment provides information that does not appear simply in the movements of the body. Overall, including vision results in 7.9% and 7.0% improvement in root mean squared error of knee and ankle angle predictions respectively. The improvement in Pearson Correlation Coefficient for knee and ankle predictions is 1.5% and 12.3% respectively. We discuss particular moments where vision greatly improved, or failed to improve, the prediction performance. We also find that the benefits of vision can be enhanced with more data. Lastly, we discuss challenges of continuous estimation of gait in natural, out-of-the-lab datasets.

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

Prosthetic limb controllers employ discrete modes for well-defined scenarios such as stair ascent, stair descent, or ramps. General human locomotion, however, is a continuous motion, fluidly adapting to the environment and not always categorizable into modes. It exhibits strong inter-joint coordination and the movement of a single joint can be largely predicted based on the movement of the rest of the body. We show that using body motion from the intact limbs and trunk, a reference trajectory can be generated for a prosthetic joint for every instant in time. Previously we demonstrated that a Recurrent Neural Network (RNN) can predict ankle angle trajectory for structured activities. In this study, we apply a similar network to more unstructured activities which are hard to categorize into modes. A wearable motion capture suit was worn by 10 healthy subjects to record full body kinematics during obstacle avoidance, sidestepping, weaving through cones, and backward walking. We used an RNN to predict right ankle kinematics from the other joint kinematics. The model was robust to subject-specific variations such as walking speed and step length. We present the performance for different activities and using different subsets of the sensors. This system demonstrates the potential for generating a reference trajectory for a prosthesis or other rehabilitation robot without explicit featurization of terrains or gait events.

Many upper-limb prostheses lack proper wrist rotation functionality, leading to users performing poor compensatory strategies, leading to overuse or abandonment. In this study, we investigate the validity of creating and implementing a data-driven predictive control strategy in object grasping tasks performed in virtual reality. We propose the idea of using gaze-centered vision to predict the wrist rotations of a user and implement a user study to investigate the impact of using this predictive control. We demonstrate that using this vision-based predictive system leads to a decrease in compensatory movement in the shoulder, as well as task completion time. We discuss the cases in which the virtual prosthesis with the predictive model implemented did and did not make a physical improvement in various arm movements. We also discuss the cognitive value in implementing such predictive control strategies into prosthetic controllers. We find that gaze-centered vision provides information about the intent of the user when performing object reaching and that the performance of prosthetic hands improves greatly when wrist prediction is implemented. Lastly, we address the limitations of this study in the context of both the study itself as well as any future physical implementations.