Publications

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.

There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports.

A recent theoretical result proposed Sigma, a novel GM protocol that forms views using a single-round of message exchange. Prior GM protocols have required more rounds in the worst-case. In this paper, we investigate how well Sigma performs in practice. We simulate Sigma using WAN connectivity traces and compare its performance to two leading GM protocols, Moshe and Ensemble. Our simulations show, consistently with theoretical results, that Sigma always terminates within one round of message exchange, faster than Moshe and Ensemble. Moreover, Sigma has less message overhead and produces virtually the same quality of views. We also observe that view-oriented GM in dynamic WAN-like environments is practical only in applications where GM need not respond to every disconnect immediately when detected. These applications are able, and prefer, to delay GM response and ignore transient disconnects, avoiding frequent futile view changes and associated overhead. We reference some applications in this category.

Sigma, the first single-round group membership (GM) algorithm, was recently introduced and demonstrated to operate consistently with theoretical expectations in a simulated WAN environment. Sigma achieved similar quality of membership configurations as existing algorithms but required fewer message exchange rounds. We now consider Sigma in terms of scalability. Sigma involves all-to-all (A2A) type of communication among members. A2A protocols have been shown to perform worse than leader-based (LB) protocols in certain networks, due to greater message overhead and higher likelihood of message loss. Thus, although LB protocols often involve additional communication steps, they can be more efficient in practice, particularly in fault-prone networks with large numbers of participating nodes. In this paper, we present Leader-Based Sigma, which transforms the original all-to-all version into a more scalable centralized communication scheme, and discuss the rounds vs. messages tradeoff involved in optimizing GM algorithms for deployment in large-scale, fault-prone dynamic network environments.