Progressive haptic guidance for a dynamic task in a virtual training environment

This thesis presents the motivation for and implementation of a novel progressive haptic guidance scheme designed to improve the efficiency of a virtual training environment used for skill acquisition. A detailed expertise-based analysis of the dynamic human motor task identifies the key skills required for success and motivates the progressive haptic guidance scheme. The thesis compares the effectiveness of the scheme to similar visual guidance, written guidance and no-guidance. The experimental training protocol presents a target-hitting training task in a virtual environment that utilizes an LCD display for visual feedback and a force feedback joystick for haptic interactions. This protocol lasts eleven sessions over a two-month period, thereby ensuring the performance saturation of participants. During each session, the number of target hits obtained becomes the objective measure of performance. Two additional measures, trajectory error and input frequency, are defined and implemented to calculate the performance of participants in two key skills. The guidance scheme then employs these last two measures as gain inputs to the guidance controller, which in turn progressively diminishes the forces that display guidance as virtual walls. The haptic controller design initially restricts a participant's motion to a preferred task path, but increased performance results in decreased guidance from one trial to the next. In addition to these measures, the protocol also presents the computerized version of the NASA Task Load Index (TLX) to all participants at each session, thereby providing cognitive workload measurements throughout the entire training period. The results demonstrate that this progressive haptic guidance scheme, one that integrates key skills and measures of performance, significantly outperforms three other guidance modes early on in the training and only when guidance is active. The data failed to show whether the haptic guidance scheme has significantly higher performance when the guidance is inactive. This scheme also generates less frustration and mental workload than visual guidance. Possible applications for these findings include virtual training environments designed for surgery and rehabilitation

An Adaptable Human-Like Gait Pattern Generator Derived From a Lower Limb Exoskeleton

Walking rehabilitation processes include many repetitions of the same physical movements in order to replicate, as close as possible, the normal gait trajectories, and kinematics of all leg joints. In these conventional therapies, the therapist′s ability to discover patient′s limitations—and gradually reduce them—is key to the success of the therapy. Lower-limb robotic exoskeletons have strong deficiencies in this respect as compared to an experienced therapist. Most of the currently available robotic solutions are not able to properly adapt their trajectories to the biomechanical limitations of the patient. With this in mind, much research and development is still required in order to improve artificial human-like walking patterns sufficiently for valuable clinical use. The work herein reported develops and presents a method to acquire and saliently analyze subject-specific gait data while the subject dons a passive lower-limb exoskeleton. Furthermore, the method can generate adjustable, yet subject-specific, kinematic gait trajectories useful in programming controllers for future robotic rehabilitation protocols. A human-user study with ten healthy subjects provides the experimental setup to validate the proposed method. The experimental protocol consists in capturing kinematic data while subjects walk, with the donned H2 lower-limb exoskeleton, across three experimental conditions: walking with three different pre-determined step lengths marked on a lane. The captured ankle trajectories in the sagittal plane were found by normalizing all trials of each test from one heel strike to the next heel strike independent of the specific gait features of each individual. Prior literature suggests analyzing gait in phases. A preliminary data analysis, however, suggests that there exist six key events of the gait cycle, events that can adequately characterize gait for the purposes required of robotic rehabilitation including gait analysis and reference trajectory generation. Defining the ankle as an end effector and the hip as the origin of the coordinate frame and basing the linear regression calculations only on the six key events, i.e., Heel Strike, Toe Off, Pre-Swing, Initial Swing, Mid-Swing, and Terminal Swing, it is possible to generate a new calculated ankle trajectory with an arbitrary step length. The Leave-One-Out Cross Validation algorithm was used to estimate the fitting error of the calculated trajectory vs. the characteristic captured trajectory per subject, showing a fidelity average value of 95.2, 96.1, and 97.2%, respectively, for each step-length trial including all subjects. This research presents method to capture ankle trajectories from subjects and generate human-like ankle trajectories that could be scaled and computed on-line, could be adjusted to different gait scenarios, and could be used not only to generate reference trajectories for gait controllers, but also as an accurate and salient benchmark to test the human likeness of gait trajectories employed by existing robotic exoskeletal devices