Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

Abstract

This paper is the second in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. In this work, we track the hand of healthy individuals as they perform a variety of activities of daily living (ADLs) in three ways decoupled from hand orientation: end-point locations of the hand trajectory, whole path trajectories of the hand, and straight-line paths generated using start and end points of the hand. These data are examined by a clustering procedure to reduce the wide range of hand use to a smaller representative set. Hand orientations are subsequently analyzed for the end-point location clustering results and subsets of orientations are identified in three reference frames: global, torso, and forearm. Data driven methods that are used include dynamic time warping (DTW), DTW barycenter averaging (DBA), and agglomerative hierarchical clustering with Ward's linkage. Analysis of the endpoint locations, path trajectory, and straight-line path trajectory identified 5, 5, and 7 ADL task categories, respectively, while hand orientation analysis identified up to 4 subsets of orientations for each task location, discretized and classified to the facets of a rhombicuboctahedron. Together these provide insight into our hand usage in daily life and inform an implementation in prosthetic or robotic devices using sequential control.

The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data

“…According to the law of occurrence of sports injuries, the principal component analysis method is used to obtain the corresponding matrix for the internal factors affecting the standardization of individual data, calculate the eigenvalues of the matrix and the eigenvalues of the corresponding eigenvectors, and calculate the variance of the principal components from high to low for each contribution rate. ese main parameters influence the injury rehabilitation individual intrinsic factors [15].…”

A continuous statistical-geometric framework for normative and impaired gaits

“…We introduced the concept of difference curves to cluster on changes in joint kinematic trajectories between EXO + and EXO − gait, thereby capturing characteristics of user strategy in response to external assistance. While hierarchical clustering has been used to successfully subgroup post-stroke joint kinetics [13] and upper limb motions in unimpaired individuals [48], here we extended these approaches to evaluate user response to exosuit assistance and used a continuous representation of change for this analysis. Similar to our first application, our geometric approach to representing gait enabled us to account for variations in posture of the user throughout the gait cycle without the need for feature engineering.…”

Trajectory Control–An Effective Strategy for Controlling Multi-DOF Upper Limb Prosthetic Devices

“…While we hope the comparison stands, further improvements to the cognitive burden and robustness of operating several DOF simultaneously [6], [13] could ultimately outweigh the simplicity of trajectory control. Finally, it is likely that endpoint control is what humans use to program reaching and grasping movements, so the idea of using a predefined decoupled set of locations and orientations, rather than joint motions, could be a possible future direction [44]. In the meantime, trajectory control highlights the benefits of using learned motions and suggests that a complex, yet practical, solution will likely be a semi-autonomous one and where additional trajectories can be added by the users.…”