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Asymmetries in interpersonal coordination: recruiting degrees-of-freedom stabilizes coordination

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The current paper presents two studies that examine how asymmetries during interpersonal coordination are compensated for. It was predicted that destabilizing effects of asymmetries are stabilized through the recruitment and suppression of motor degrees-of-freedom (df). Experiment 1 examined this effect by having participants coordinate line movements of different orientations. Greater…

The current paper presents two studies that examine how asymmetries during interpersonal coordination are compensated for. It was predicted that destabilizing effects of asymmetries are stabilized through the recruitment and suppression of motor degrees-of-freedom (df). Experiment 1 examined this effect by having participants coordinate line movements of different orientations. Greater differences in asymmetries between participants yielded greater spatial deviation, resulting in the recruitment of df. Experiment 2 examined whether coordination of movements asymmetrical in shape (circle and line) yield simultaneous recruitment and suppression of df. This experiment also tested whether the initial stability of the performed movement alters the amount of change in df. Results showed that changes in df were exhibited as circles decreasing in circularity and lines increasing in circularity. Further, more changes in df were found circular (suppression) compared to line (recruitment) movements.
ContributorsFine, Justin (Author) / Amazeen, Eric L (Thesis advisor) / Amazeen, Polemnia G (Committee member) / Brewer, Gene A. (Committee member) / Arizona State University (Publisher)
Created2013
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Convergence results for two models of interaction

Description
I investigate two models interacting agent systems: the first is motivated by the flocking and swarming behaviors in biological systems, while the second models opinion formation in social networks. In each setting, I define natural notions of convergence (to a ``flock" and to a ``consensus'', respectively), and study the convergence…

I investigate two models interacting agent systems: the first is motivated by the flocking and swarming behaviors in biological systems, while the second models opinion formation in social networks. In each setting, I define natural notions of convergence (to a ``flock" and to a ``consensus'', respectively), and study the convergence properties of each in the limit as $t \rightarrow \infty$. Specifically, I provide sufficient conditions for the convergence of both of the models, and conduct numerical experiments to study the resulting solutions.
ContributorsTheisen, Ryan (Author) / Motsch, Sebastien (Thesis advisor) / Lanchier, Nicholas (Committee member) / Kostelich, Eric (Committee member) / Arizona State University (Publisher)
Created2018
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Idiographic Models of Walking Behavior for Personalized mHealth Interventions: Some Novel Approaches

Description
This thesis presents the development of idiographic models (i.e., single subject or N = 1) of walking behavior as a means of facilitating the design of control systems to optimize mobile health (mHealth) interventions for sedentary adults. Model-on-Demand (MoD), an adaptive modeling technique, is demonstrated as an ideal method for…

This thesis presents the development of idiographic models (i.e., single subject or N = 1) of walking behavior as a means of facilitating the design of control systems to optimize mobile health (mHealth) interventions for sedentary adults. Model-on-Demand (MoD), an adaptive modeling technique, is demonstrated as an ideal method for modeling nonlinear systems with noise on a simulated continuously stirred tank reactor (CSTR). Comparing MoD to AutoRegressive with eXogenous input (ARX) estimation, MoD outperforms ARX in terms of addressing both nonlinearity and noise in the CSTR system. With the CSTR system as an initial proof of concept, MoD is then used to model individual walking behavior using intervention data from participants of HeartSteps, a walking intervention that studies the effect of within-day suggestions. Given the number of possible measured features from which to design the MoD models, as well as the number of model parameters that influence the model’s performance, optimizing MoD models through exhaustive search is infeasible. Consequently, a discrete implementation of simultaneous perturbation stochastic approximation (DSPSA) is shown to be an efficient algorithm to find optimal models of walking behavior. Combining MoD with DSPSA, models of walking behavior were developed using participant data from Just Walk, a day-to-day walking intervention; MoD outperformed ARX models on both estimation and validation data. DSPSA was also applied to ARX modeling, highlighting the use of DSPSA to not only search over model parameters and features but also data partitioning, as DSPSA was used to evaluate models under various combinations of estimation and validation data from a single participant’s walking data. Results of this thesis point to ARX with DSPSA as a routine means for dynamic model estimation in large-scale behavioral intervention settings.
ContributorsKha, Rachael T (Author) / Rivera, Daniel E (Thesis advisor) / Deng, Shuguang (Committee member) / Muhich, Christopher (Committee member) / Arizona State University (Publisher)
Created2022