Related publications (16)

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...
New York2023

Temporal analysis of multimodal data to predict collaborative learning outcomes

Kshitij Sharma, Jennifer Kaitlyn Olsen

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated ...
WILEY2020

A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry

Davide Scaramuzza

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to ...
2019

Measurement-supported performance assessment of earthquake-damaged concrete and masonry structures

Yves Sylvain Gilles Reuland

As demonstrated by recent events in Italy, New-Zealand, Haiti, and Nepal, earthquakes continue to pose threats to civil infrastructure, including buildings. For a long time seismic ultimate limit states have not been considered in design codes for regions ...
EPFL2017

Effectiveness of different sensing modalities in predicting targets of reaching movements

Human motion recognition is essential for many biomedical applications, but few studies compare the abilities of multiple sensing modalities. This paper thus evaluates the effectiveness of different modalities when predicting targets of human reaching move ...
2013

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.