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ContributorsBriehl, Matthew Alexander (Performer) / ASU Library. Music Library (Publisher)
Created2021-10-22
ContributorsZweig, Phillip (Performer) / ASU Library. Music Library (Publisher)
Created2021-10-31
ContributorsLeisner, David (Performer) / ASU Library. Music Library (Publisher)
Created2021-11-04
ContributorsBriehl, Matthew Alexander (Performer) / Brady, Isaac (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-16
ContributorsRamirez Corral, Jessica (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-28
ContributorsZhang, Yifan (Performer) / Chen, Wei-Jhen (Performer) / Wu, Yu (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-14
ContributorsDominguez, Ramon (Performer) / ASU Library. Music Library (Publisher)
Created2022-04-28
Description
This thesis work presents two separate studies:The first study assesses standing balance under various 2-dimensional (2D) compliant environments simulated using a dual-axis robotic platform and vision conditions. Directional virtual time-to-contact (VTC) measures were introduced to better characterize postural balance from both temporal and spatial aspects, and enable prediction of fall-relevant directions. Twenty healthy young adults were recruited to perform quiet standing tasks on the platform. Conventional stability measures, namely center-of-pressure (COP) path length and COP area, were also adopted for further comparisons with the proposed VTC. The results indicated that postural balance was adversely impacted, evidenced by significant decreases in VTC and increases in COP path length/area measures, as the ground compliance increased and/or in the absence of vision (ps < 0.001). Interaction effects between environment and vision were observed in VTC and COP path length measures (ps ≤ 0.05), but not COP area (p = 0.103). The estimated likelihood of falls in anterior-posterior (AP) and medio-lateral (ML) directions converged to nearly 50% (almost independent of the foot setting) as the experimental condition became significantly challenging.
The second study introduces a deep learning approach using convolutional neural network (CNN) for predicting environments based on instant observations of sway during balance tasks. COP data were collected from fourteen subjects while standing on the 2D compliant environments. Different window sizes for data segmentation were examined to identify its minimal length for reliable prediction. Commonly-used machine learning models were also tested to compare their effectiveness with that of the presented CNN model. The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length data, which cannot be achieved by traditional machine learning models (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition.
These two studies provide new perspectives in human postural balance, which cannot be
interpreted by conventional stability analyses. Outcomes of these studies contribute to the advancement of human interactive robots/devices for fall prevention and rehabilitation.
ContributorsPhan, Vu Nguyen (Author) / Lee, Hyunglae (Thesis advisor) / Peterson, Daniel (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2021
Description
The goal of this thesis research is to contribute to the design of set-valued methods, i.e., algorithms that leverage a set-theoretic framework that can provide a powerful means for control designs for general classes of uncertain nonlinear dynamical systems, and in particular, to develop set-valued algorithms for constrained reachability problems and estimation.I propose novel fixed-order hyperball-valued observers for different classes of nonlinear systems, including Linear Parameter Varying, Lipschitz continuous and Decremental Quadratic Constrained nonlinearities, with unknown inputs that simultaneously find bounded sets of states and unknown inputs that contain the true states and inputs and are compatible with the measurement/outputs. In addition, I provide sufficient conditions for the existence and stability of the estimates, the convergence of the estimation errors, and the optimality of the observers.
Moreover, I design state and unknown input observers, as well as mode detectors for hidden mode, switched linear and nonlinear systems with bounded-norm noise and unknown inputs. To address this, I propose a multiple-model approach to obtain a bank of mode-matched set-valued observers in combination with a novel mode observer, based on elimination. My mode elimination approach uses the upper bound of the norm of to-be-designed residual signals to remove inconsistent modes from the bank of observers. I also provide sufficient conditions for mode detectability.
Furthermore, I address the problem of designing interval observers for partially unknown nonlinear systems, using affine abstractions, nonlinear decomposition functions, and a data-driven function over-approximation approach to over-estimate the unknown dynamic model. The proposed observer recursively computes the correct interval estimates. Then, using observed measurement signals, the observer iteratively shrinks the intervals. Moreover, the observer updates the over-approximation model
of the unknown dynamics.
Finally, I propose a tractable family of remainder-from decomposition functions for
a broad range of dynamical systems. Moreover, I introduce a set-inversion algorithm that along with the proposed decomposition functions have several applications, e.g., in the approximation of the reachable sets for bounded-error, constrained, continuous, and/or discrete-time systems, as well as in guaranteed state estimation. Leveraging mixed-monotonicity, I provide novel set-theoretic approaches to address the problem of polytope-valued state estimation in bounded-error discrete-time nonlinear systems, subject to nonlinear observations/constraints.
ContributorsKhajenejad, Mohammad (Author) / Zheng Yong, Sze S.Z.Y (Thesis advisor) / Nedich, Angelia A.N (Committee member) / Reffett, Kevin K.R (Committee member) / M. Berman, Spring S.M.B (Committee member) / Fainekos, Georgios G.F (Committee member) / Lee, Hyunglae H.L (Committee member) / Arizona State University (Publisher)
Created2021