Quantification and visualization of coordination during non-cyclic upper extremity motion

There are many design challenges in creating at-home tele-monitoring systems that enable quantification and visualization of complex biomechanical behavior. One such challenge is robustly quantifying joint coordination in a way that is intuitive and supports clinical decision-making. This work defines a new measure of coordination called the relative coordination metric (RCM) and its accompanying normalization schemes. RCM enables quantification of coordination during non-constrained discrete motions. Here RCM is applied to a grasping task. Fifteen healthy participants performed a reach, grasp, transport, and release task with a cup and a pen. The measured joint angles were then time-normalized and the RCM time-series were calculated between the shoulder-elbow, shoulder-wrist, and elbow-wrist. RCM was normalized using four differing criteria: the selected joint degree of freedom, angular velocity, angular magnitude, and range of motion. Percent time spent in specified RCM ranges was used as. a composite metric and was evaluated for each trial. RCM was found to vary based on: (1) chosen normalization scheme, (2) the stage within the task, (3) the object grasped, and (4) the trajectory of the motion. The RCM addresses some of the limitations of current measures of coordination because it is applicable to discrete motions, does not rely on cyclic repetition, and uses velocity-based measures. Future work will explore clinically relevant differences in the RCM as it is expanded to evaluate different tasks and patient populations. Keywords: Coordination; Tele-rehabilitation; Grasp; Upper extremity; Performance metricsNational Science Foundation (U.S.) (Award IIS-1453141)United States. National Aeronautics and Space Administration (Award NCC 9-58)United States. National Aeronautics and Space Administration (Award NNX16AM71H

Development of astronaut reorientation methods : a computational and experimental study

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 143-150).Past spaceflight missions have shown that astronauts adapt their motor-control strategies to the microgravity environment. Even though astronauts undergo hundreds of training hours, the strategies for locomotion and orientation are not specifically prescribed. The majority of an astronaut's motion-control strategies are developed underwater. While underwater training can be beneficial in certain aspects, such as learning which orientations an astronaut will encounter and becoming familiar with task timelines, it is not effective for self-learned motor-control strategies. Further, the development of unfamiliar tasks, such as reorienting without external forces, will most likely not occur naturally. Self-rotations -- human-body rotations without external torques -- are not only helpful for reducing adaptation time, but can be a crucial safety countermeasure during extravehicular activity. In this thesis, computational and experimental methods are developed to create and analyze astronaut reorientation methods. The computational development of control methods for human motion planning offers a novel way to provide astronauts with maneuvers that are difficult to obtain experimentally in Earth gravity (1-G). Control of human-body dynamics can be posed as a motion-planning problem for which many different solution methods exist. This research considers two different frameworks -- quantized control and optimal control. The quantized control method permits the development of complete maneuvers that are appropriate for humans to perform in high-stress situations by defining a set of specific finite-time trajectories called motion primitives. The implementation of an optimal control method allows for the refinement and further understanding of maneuver characteristics with an emphasis on how the central nervous system controls motion.(cont.) Human rotation experiments provide further insight into the complexity of self-rotation techniques and a way to study the effects of training in a rigorous and realistic manner.by Leia Abigail Stirling.Ph.D

An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors

Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study (n = 15) with an absolute root-mean-square-error (RMSE) of 9.24∘ and a zero-mean RMSE of 3.49∘. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data.National Science Foundation (U.S.) (GRFP)National Science Foundation (U.S.) (IIS-1453141

Human-robot co-navigation using anticipatory indicators of human walking motion

Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation

Dynamic Human-Centered Suit Design: A Computational and Experimental Method

No abstract availabl

Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking

Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking)

Ankle exoskeleton torque controllers based on soleus muscle models.

Powered exoskeletons are typically task-specific, but to facilitate their wider adoption they should support a variety of tasks, which requires generalizeable controller designs. In this paper, we present two potential controllers for ankle exoskeletons based on soleus fascicles and Achilles tendon models. The methods use an estimate of the adenosine triphosphate hydrolysis rate of the soleus based on fascicle velocity. Models were evaluated using muscle dynamics from the literature, which were measured with ultrasound. We compare the simulated behavior of these methods against each other and to human-in-the-loop optimized torque profiles. Both methods generated distinct profiles for walking and running with speed variations. One of the approaches was more appropriate for walking, while the other approach estimated profiles similar to the literature for both walking and running. Human-in-the-loop methods require long optimizations to set parameters per individual for each specific task, the proposed methods can produce similar profiles, work across walking and running, and be implemented with body-worn sensors without requiring torque profile parameterization and optimization for every task. Future evaluations should examine how human behavior changes due to external assistance when using these control models