Wikipedia Reader Navigation: When Synthetic Data Is Enough

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.

Towards real-world biomechanical analysis of performance and functional capacity using wearable sensors

Salil Apte

Today, wearable heart rate monitors are an integral part of runners' training sessions, with heart rate data routinely used to assess effort intensity and stress on the body. As athletes translate their physical capacity into performance on the field through their movement, biomechanical assessment can provide valuable information that complements physiological assessment. However, the potential of using biomechanical information in the evaluation of training sessions and standardized tests in practice remains largely untapped, partly because the assessment devices remain cumbersome to use and often require long post-processing, as well as programming skills. The proposed work aims to realize this potential by developing field methods for performance and capacity evaluation using portable inertial measurement units (IMUs) and Global Navigation Satellite System (GNSS) receivers.Running performance can be characterized by the ability to maintain appropriate running technique despite fatigue, while keeping the effort intensity prescribed by the coach, or planned as a pacing strategy. In this work, a systematic review was conducted to examine and synthesize the results of fatigue protocols in running, fol-lowed by continuous measurements during a competition to confirm the trends obtained from the review with data from the field and to measure the changes in running technique due to fatigue. In addition, models were developed to accurately estimate running power using foot-worn IMU over a range of speeds and inclines and validated using gold standard methods in the laboratory, to better characterize running intensity. The second part of this work consisted of investigating the ability of IMUs and GNSS to improve the evaluation of athletes in standardized tests, referred to as their functional capacity. Functional capacity is typically used by coaches to develop appropriate training loads for athletes. This work presented validated methods to instrument common functional tests with wearable sensors to measure the speed, agility, and endurance of athletes in the field. In addition, these methods enable the extraction and a deeper analysis of relevant biomechanical parameters that contribute to the measured capacity and help the sporting staff understand athletes' strengths and weaknesses in detail. All of the research conducted and methods developed in this work are based on various combinations of a minimal body-worn sensor setup with foot-worn IMUs and a single trunk-worn IMU-GNSS unit. The signal processing algorithms and models developed in this work allow the recorded signals to be translated into easily interpretable and actionable information. Based on this information, coaches and physical therapists can develop customized training programs that target the relevant parameters. The proposed sensor setups and methods have been used and validated in a variety of situations, such as pre-season testing of a professional soccer team, training sessions of elite sprinters, the Lausanne half-marathon race, etc., highlighting their potential for real-world application. I believe that this work will help pave the way towards a deeper understanding of the biomechanical contributions to performance in running and provide new tools for the development of personalized training and rehabilitation programs, with the goal of optimizing positive adaptation to training stimuli, thereby improving performance and reducing the incidence of injury.

EPFL2022

Towards real-world biomechanical analysis of performance and functional capacity using wearable sensors

Salil Apte

EPFL2022