Diego Felipe Paez Granados's picture
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First Name: 
Diego Felipe
Last Name: 
Paez Granados
Affiliation: 
ETH Zurich
Job Title: 
Head of SCAI Lab, Senior researcher
Expertise: 
Robotics, Human-Robot Interaction, Exoskeletons, Mechatronics, Control Systems
Short Bio: 
Diego is the Head of the Spinal Cord Injury Artificial Intelligence (SCAI) Lab at the Swiss Paraplegic Center in Nottwil. The lab, established in 2022, focuses on prognosis, sensing and modeling for personalized health care of patients with a spinal cord injury. Diego received the Japanese MEXT scholarship from 2012 to 2017 to follow his Master's and PhD studies in Bioengineering and Robotics at Tohoku University, Japan. His research focuses on physical and cognitive human modeling, control for human-robot interaction, soft-robot design, and control with human-in-the-loop. He conducted Postdoctoral studies in assistive robotics from 2017 to 2018 at the Artificial Intelligence Lab at the University of Tsukuba in Japan. Then he worked in human sensing and modeling for robot control and safety at the Learning Algorithms and System Lab (LASA) at EPFL, Switzerland from 2019 to 2021. He is a grantee of the Toyota Mobility Unlimited Challenge (2018 and 2019), which lead to the start-up Qolo Inc., a company innovating personal mobility solutions and rehabilitation equipment for lower-body impairments. Diego is highly passionate about modeling the human body and achieving a patient digital twin to improve prognosis, build novel technologies, and assist healthcare workers and caregivers.

Datasets & Competitions

The current dataset – crowdbot – presents outdoor pedestrian tracking from onboard sensors on a personal mobility robot navigating in crowds. The robot Qolo, a personal mobility vehicle for people with lower-body impairments was equipped with a reactive navigation control operating in shared-control or autonomous mode when navigating on three different streets of the city of Lausanne, Switzerland during farmer’s market days and Christmas market. Full Dataset here: DOI:10.21227/ak77-d722

Instructions: 

Download the data files and put them under data (place the uncompressed files or symbolic links)
cd path/to/crowdbot_tools/data
# (recommended) create symbolic links of rosbag folder instead of copying data:
ln -s /data_qolo/lausanne_2021/24_04_2021/shared_control 0424_shared_control
ln -s /data_qolo/lausanne_2021/24_04_2021/RDS/detector 0424_rds_detectorThe used file structure is as follows:

Examples of how to access the data can be found in the open repository: https://github.com/epfl-lasa/crowdbot-evaluation-tools 

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This dataset presents collisions between a service robot - Qolo - and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present a set of collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children.

Instructions: 

This dataset contains the following main files:

 

  • collision_test_rawdata.zip:  This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2. ‘test_name’/01_values/’testName’_CFC1000.xlsx
  • collision_test_analysis.zip: This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.‘test_name’/01_values/’testName’_Analysis_v2.xlsx
  • collision_data_matlab_structure.zip: Matlab containers with all data.
  • scripts-crash-test-service-robots.zip: processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: https://github.com/epfl-lasa/crash-tests-service-robots
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