Improving Automatic Control of Upper-Limb Prosthesis Wrists Using Gaze-Centered Eye Tracking and Deep Learning.

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.

Descending 13 real world steps: A dataset and analysis of stair descent.

BACKGROUND: Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available. RESEARCH QUESTION: We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior. METHODS: We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps. RESULTS: We report the lower limb joint angle ranges (30° and 8° for hip flexion and extension, 85° and -11° for knee flexion and extension, and 31° and 28° for ankle dorsi- and plantar-flexion). The self-selected speed was 0.79 ± 0.16 m/s, with cycle duration of 0.97 ± 0.18 s. Mean foot overhang as a percentage of foot length was 17.07 ± 6.66 %, and we calculate that foot size explains only 6% of heel placement variation, but 79% of toe placement variation. We also find a minor but significant asymmetry between left and right maximum hip flexion angle, though all other measured parameters were symmetrical. SIGNIFICANCE: This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. This study enables analysis of gait characteristics including self-selected walking speed and foot placement to better understand typical stair gait behavior. The dataset is a public resource for understanding typical stair descent.

Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.

We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).